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ASME Conference Presenter Attendance Policy and Archival Proceedings

2016;():V002T00A001. doi:10.1115/MSEC2016-NS2.
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This online compilation of papers from the ASME 2016 11th International Manufacturing Science and Engineering Conference (MSEC2016) represents the archival version of the Conference Proceedings. According to ASME’s conference presenter attendance policy, if a paper is not presented at the Conference by an author of the paper, the paper will not be published in the official archival Proceedings, which are registered with the Library of Congress and are submitted for abstracting and indexing. The paper also will not be published in The ASME Digital Collection and may not be cited as a published paper.

Commentary by Dr. Valentin Fuster

Materials: Advances in Experiments and Modeling of Micromechanics and Microstructure Evolution in Manufacturing Processes

2016;():V002T01A001. doi:10.1115/MSEC2016-8503.

Surface scratches and residual stresses inevitably appear on the surface of the component as a result of the machining process. The damage evolution of surface scratch due to the combined effect of cyclic loading and residual stresses will be significantly different from the case where only the cyclic loading is considered. In the damage evolution of surface scratch, the short crack growth is of great importance owing to its apparently anomalous behaviors compared with the long-crack growth. In this paper, the effect of the surface roughness and the residual stress on the short crack growth is studied. Firstly, the surface roughness and the residual stress of 7075-T6 aluminum alloy induced by the high speed milling process with various cutting speeds and feed rates are investigated with the experimental method. The maximum height roughness parameter is measured, which is regarded as the surface defect induced by the milling process. The residual stress on the specimen surface is measured with the X-ray diffraction. Results show that the surface roughness becomes higher with the increase of the feed rate. However, the influence of the cutting speed on the surface roughness is not significant. The residual stresses on the specimen surface are all in the compressive state. The residual stress is more compressive as the feed rate increases. The effects of the process parameters on the surface roughness and the residual stress are described by the fitted formulas. Then a modified model is built to characterize short fatigue crack growth behaviors with the consideration of the residual stress. This model is proved to provide a realistic treatment of the short crack growth, as reflected by comparison with experimental fatigue crack growth data of medium carbon steel and 7075-T6 aluminum alloy published in literature. The effect of surface roughness and residual stress caused by the milling process on the short crack growth is also investigated by using the proposed model. The growth of the scratch is nonlinear when it is subjected to the cyclic load. The compressive residual stress reduces the growth rate of the crack. The crack with larger initial surface roughness grows faster than that with smaller roughness. The correlation of surface roughness, residual stress and crack growth length is obtained by the polynomial fitting. The investigations in this paper can help the damage tolerance design of structures and improve the awareness of the effect of the residual stress and surface roughness induced by the machining process on the short crack growth.

Commentary by Dr. Valentin Fuster
2016;():V002T01A002. doi:10.1115/MSEC2016-8580.

The mechanical and microstructural properties of FV520B martensitic stainless steel fabricated by laser hot-wire deposition are presented. An investigation based on experimental method was conducted to analyze the development of microstructure and microhardness under multiple laser heating. Multiple layers were cladded on the surface of martensitic stainless steel FV520B by fiber laser. A defect-free and high forming quality coatings were obtained. The microstructure of clad layer and heat affected zone was characterized using an optical microscope, SEM and EBSD. The gradient microhardness from the cladding layer to the substrate was tested. Subsequently, the effect of thermal history under multi-layer laser heating on the microstructure and microhardness was analyzed. Results indicate that the hardening trend in the coating/substrate interface and softening trend in the heat affected zone under laser heating. The tempering effect of the following-layer laser heating facilitates the reprecipitation of the hardening phases in heat affected zone.

Commentary by Dr. Valentin Fuster
2016;():V002T01A003. doi:10.1115/MSEC2016-8594.

Selective laser melting (SLM) is an additive manufacturing technique in which complex parts can be fabricated directly by melting layers of powder from a CAD model. SLM has a wide range of application in biomedicine and other engineering areas and it has a series of advantages over traditional processing techniques. A large number of variables including laser power, scanning speed, scanning line spacing, layer thickness, material based input parameters, etc. have a considerable effect on SLM process materials. The interaction between these parameters is not completely studied. Limited studies on balling effect in SLM, densifications under different processing conditions, and laser re-melting, have been conducted that involved microstructural investigation. Grain boundaries are amongst the most important microstructural properties in polycrystalline materials with a significant effect on the fracture and plastic deformation. In SLM samples, in addition to the grain boundaries, the microstructure has another set of connecting surfaces between the melt pools. In this study, a computational framework is developed to model the mechanical response of SLM processed materials by considering both the grain boundaries and melt pool boundaries in the material. To this end, a 3D finite element model is developed to investigate the effect of various microstructural properties including the grains size, melt pools size, and pool connectivity on the macroscopic mechanical response of the SLM manufactured materials. A conventional microstructural model for studying polycrystalline materials is modified to incorporate the effect of connecting melt pools beside the grain boundaries. In this model, individual melt pools are approximated as overlapped cylinders each containing several grains and grain boundaries, which are modeled to be attached together by the cohesive zone method. This method has been used in modeling adhesives, bonded interfaces, gaskets, and rock fracture. A traction-separation description of the interface is used as the constitutive response of this model. Anisotropic elasticity and crystal plasticity are used as constitutive laws for the material inside the grains. For the experimental verification, stainless steel 316L flat dog bone samples are fabricated by SLM and tested in tension. During fabrication, the power of laser is constant, and the scan speed is changed to study the effect of fabrication parameters on the mechanical properties of the parts and to compare the result with the finite element model.

Commentary by Dr. Valentin Fuster
2016;():V002T01A004. doi:10.1115/MSEC2016-8609.

Effects of starting microstructure on deformation behavior of commercially pure Ni blanks during micro-deep drawing was studied utilizing microforming set up that sits inside the chamber of Scanning Electron Microscopy (SEM) enables in situ observation of material flow during deformation. Various microstructure fields were created in Ni blanks using Severe Plastic Deformation (SPD) and heat treatment. SEM based Digital Image correlation (DIC) technique was used to characterize the micromechanics of deformation and its relation to process outcomes/performance. Pre and post–deformation microstructure analysis was carried out by performing Orientation Imaging Microscopy (OIM) to track the microstructure evolution across the micro-formed blanks during deformation in order to identify the process anomalies originated from the characteristics of starting microstructure and its interaction with deformation mechanics. We showed that microstructurally graded sheets consisting of nano-grained/coarse-grained layers significantly improves the formability of micro-blanks and effectively delays strain localization and onset of instability/failure during micro-deep drawing.

Commentary by Dr. Valentin Fuster
2016;():V002T01A005. doi:10.1115/MSEC2016-8689.

In this study, we investigated the mechanical properties of AZ31B Mg alloy before and after laser shock peening (LSP). The hardness of the AZ31B Mg alloy increased from 57 HV to 69 HV after LSP. The yield strength increased from 128 MPa to 152 MPa. Wear resistance was significantly improved after LSP. Immersion testing showed that LSP did not significantly increase the element release and weight loss in simulated body fluid. We have demonstrated that LSP is an effective way to improve the mechanical properties of the AZ31B Mg alloy.

Commentary by Dr. Valentin Fuster
2016;():V002T01A006. doi:10.1115/MSEC2016-8701.

A powerful surface severe plastic deformation (SSPD) technique, ultrasonic nanocrystal surface modification (UNSM) has been used to treat pure iron to induce surface nanocrystallization. Transmission electron microscopy and surface profiler were used to study the microstructure and surface roughness after UNSM. Results indicate that the surface nanocrystallization with the controllable surface roughness was obtained. After that, gas nitriding of the nanocrystalline and microcrystalline iron was carried out and compared. X-ray diffraction, micro hardness testing and energy dispersive spectroscopy were applied to investigate the phase, micro hardness and distribution of nitrogen atoms in the iron sample after nitriding. It has been found that nitriding efficiency has been significantly improved in UNSM-processed samples than that in the non-processed samples as manifested by higher hardness and higher volume fraction of the nitride phase. With appropriate nanocrystallization, nitriding can occur efficiently at temperature as low as 300 °C.

Commentary by Dr. Valentin Fuster
2016;():V002T01A007. doi:10.1115/MSEC2016-8703.

A novel surface technique, ultrasonic nano-crystal surface modification (UNSM), was used to process BMGs (Bulk Metallic Glasses) to improve the fracture strength and strain. It has been found that the fracture strength and strain are increased by 14.6% and 15.3% respectively after UNSM. In addition, micro-defects (voids and cracks) are found at the surface layer of UNSM-treated specimen after fracture, while there are fewer micro-defects observed in the untreated sample. We attribute the improvement of fracture stress and strain to two mechanisms. First, the excess free volume generated in BMGs by UNSM helps the nucleation of shear bands and the redistribution of the localized deformation. Second, the compressive residual stress generate by UNSM can slow down crack propagation. Both mechanisms lead to the improvement of BMGs fracture strength and strain.

Commentary by Dr. Valentin Fuster
2016;():V002T01A008. doi:10.1115/MSEC2016-8705.

In this study, an innovative process called nanocrystallization-assisted nitriding was used to process 4140 steels. First, a nanocrystalline surface layer was induced in 4140 steel by ultrasonic nanocrystal surface modification (UNSM). The abundant nanoscale grain boundaries provide micro-channels for efficient nitrogen diffusion during nitriding at relatively low temperature (450 °C) and short duration (4 hours). The samples were characterized by X-ray diffraction, scanning electron microscopy and energy dispersive spectroscopy. The hardness and corrosion resistance were examined and compared for samples after different processing conditions. It has been demonstrated that the sample processed by nanocrystallization-assisted nitriding has much higher hardness and corrosion resistance compared with the samples processed by nitriding only.

Topics: Steel , Nitriding , Corrosion
Commentary by Dr. Valentin Fuster
2016;():V002T01A009. doi:10.1115/MSEC2016-8737.

An innovative surface treatment method, ultrasonic nanocrystal surface deformation (UNSM), was used to process a magnesium (Mg) AZ31B alloy in this study. In the UNSM process, ultrasonic impacts induce plastic deformation on material surface that lead to grain refinement and surface morphology changes. The hardness, tensile strength, surface morphology and weight loss from immersion testing of the materials before and after UNSM were studied systematically. Compared with the unprocessed samples, the UNSM-processed samples show significant improvements in hardness (64%) and yield stress (43%). Surface topography results reveal that UNSM generates a smooth surface with an average roughness of 93 nm. In addition, the immersion results in cell medium demonstrate that the UNSM-processed group showed lower weight loss, especially during the early immersion period. It can thus be concluded that UNSM can significantly improve the hardness and yield strength of Mg alloys and reduce the corrosion rate, indicating that UNSM is a promising new method to enhance the mechanical properties of the degradable Mg alloys.

Commentary by Dr. Valentin Fuster
2016;():V002T01A010. doi:10.1115/MSEC2016-8778.

Miniature components with complex shape can be created by micromilling with high surface accuracy. However, for difficult-to-machine materials, such as Ti-alloys, failure of low flexural stiffness micro-tools is a big limitation. High spindle speeds (20,000 to 100,000 rpm) can be used to reduce the undeformed chip thickness and the cutting forces and hence the catastrophic failure of the tool can be avoided. This reduced uncut chip thicknesses, in some cases lower than the cutting edge radius, can result in intermittent chip formation which can lead to dynamic variation in cutting forces. These dynamic force variations coupled with low flexural rigidity of micro end mill can render the process unstable. Consequently, accurate prediction of forces and stability is essential in high-speed micromilling. Most of the previous studies reported in the literature use constant cutting coefficients in the mechanistic cutting force model which does not yield accurate results. Recent work has shown significant improvement in the prediction of cutting forces with velocity-chip load dependent coefficients but a single function velocity-chip model fails to predict the forces accurately at very high speeds (>80,000 rpm). This inaccurate force prediction affects the predicted stability boundary at those speeds. Hence, this paper presents a segmented approach wherein a function is fit for a given range of speed to determine the chip load dependent cutting coefficients. The segmented velocity-chip load cutting coefficient improves the cutting force prediction at high speeds. R2 value is found to be improved significantly (>90% for tangential cutting coefficient) which yields the better forces prediction and hence more accurate stability boundary. This paper employs two degrees of freedom (2-DOF) model with forcing functions based on segmented velocity-chip load dependent cutting coefficients. Stability lobe diagram based on 2-DOF model has been created for different speed ranges using Nyquist stability criteria. Chatter frequency ranges between 1.003 to 1.15 times the experimentally determined first modal frequency. Chatter onset has been identified via a laser displacement sensor to experimentally validate the predicted stability lobe.

Commentary by Dr. Valentin Fuster
2016;():V002T01A011. doi:10.1115/MSEC2016-8802.

Tool chatter is envisaged as a technique to create undulations on fabricated biomedical components. Herein, a-priori designed topographies were fabricated using modulate assisted machining of oxygen free high conductivity copper. Subsequently, underpinnings of microstructure evolution in this machining process were characterized using electron back scattered diffraction based orientation imaging microscopy. These underpinnings were related to the unsteady mechanical states present during modulated assisted machining, this numerically modeled using data obtained from simpler machining configurations. In this manner, relationships between final microstructural states and the underlying mechanics were found. Finally, these results were discussed in the context of unsteady mechanics present during tool chatter, it was shown that statistically predictable microstructural outcomes result during tool chatter.

Commentary by Dr. Valentin Fuster
2016;():V002T01A012. doi:10.1115/MSEC2016-8822.

Mg alloys are promising materials for bone implant applications mainly due to their low specific density, desirable stiffness and bioresorbability in the human body. Mg-Zn-Ca alloys are among the most promising materials for resorbable orthopedic fixation devices due to their superior biocompatibility. However, the mechanical and corrosion properties of the as-cast Mg-Zn-Ca alloys are insufficient. Heat treatment is a practical approach for strengthening Mg alloys especially after the fabrication of porous structures and 3D-printed components.

We have investigated heat treatment of these devices and have studied the resulting microstructure of Mg-1.6Zn-0.5Ca (wt. %) alloys by hardness, compression, scanning electron microscopy (SEM), and electrochemical and immersion corrosion tests. Mg-1.6Zn-0.5Ca alloy was prepared with high purity Mg, Zn and Ca by casting. The cast ingots were solution-treated at 510 °C for 3 h then quenched in water. The quenched ingots were age hardened in an oil bath at 200 °C for 2 h. Pure Mg, as-cast and heat-treated Mg-1.6Zn-0.5Ca alloy ingots were cut into coupons to characterize their mechanical and corrosion properties. In vitro corrosion tests were conducted in modified simulated body fluid (m-SBF) at pH 7.4 and 37 °C.

The hardness of the Mg-Zn-Ca alloy was significantly increased from 52.6 to be 66.8 HV after heat treatment. Also, the compression test results revealed that the heat-treated alloy has the highest compressive yield and ultimate strengths without significant change in stiffness and maximum strain. The mass loss of the Mg-Zn-Ca alloy by week 4 of the in vitro immersion test reduced from 174.6 mg/cm2 for the as-cast alloy to 101.7 mg/cm2 after the heat-treatment process. Heat-treatment was found to be a powerful post-shaping process not only to enhance the mechanical properties of the Mg-1.6Zn-0.5Ca (wt. %) alloy, but also to significantly improve its biocorrosion properties. Such heat-treated alloys can also be coated with biocompatible ceramics that provide additional protection from corrosion during the bone healing period (3–4 months).

Commentary by Dr. Valentin Fuster

Materials: Advances in Manufacturing of Engineered Material Systems

2016;():V002T01A013. doi:10.1115/MSEC2016-8627.

Interest in additive manufacturing has recently been spurred by the promise of multi-material printing and the ability to embed functionality and intelligence into objects. Here, we present an alternative to additive manufacturing, introducing an end-to-end workflow in which discrete building blocks are reversibly joined to produce assemblies called digital materials. We describe the design of the bulk-material building blocks and the devices that are assembled from them. Further, we detail the design and implementation of an automated assembler, which takes advantage of the digital material structure to restore positioning errors within a large tolerance. To generate assembly sequences, we use a novel CAD/CAM workflow for designing, simulating, and assembling digital materials. Finally, we evaluate the structures assembled using this process, showing that the joints perform well under varying conditions and that the assembled structures are functionally precise.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T01A014. doi:10.1115/MSEC2016-8654.

As flexible devices and machines become more ubiquitous, there is a growing need for similarly deformable electronics. Soft polymers continue to be widely used as stretchable and flexible substrates for soft electronics, and in particular, soft sensing. These soft sensors generally consist of a highly elastic substrate with embedded microchannels filled with a conductive fluid. Deforming the substrate deforms the embedded microchannels and induces a change in the electrical resistance through the conductive fluid. Microchannels, thus, are the foundation of flexible electronic devices and sensors. These microchannels have been fabricated using various methods, where the manufacturing method greatly impacts device functionality. In this paper, comparisons are made between the following methods of microchannel manufacturing: cast molding, 3D printing of the elastomer substrate itself, and laser ablation. Further processing of the microchannels into flexible electronics is also presented for all three methods. Lastly, recommended ranges of microchannel sizes and their associated reproducibility and accuracy measures for each manufacturing method are presented.

Commentary by Dr. Valentin Fuster
2016;():V002T01A015. doi:10.1115/MSEC2016-8668.

A set of monotonic tensile tests was performed on 3-D printed plastics following ASTM standards. The experiment tested a total of 13 “dog bone” test specimens where the material, infill percentage, infill geometry, load orientation, and strain rate were varied. Strength-to-weight ratios of the various infill geometries were compared. It was found through tensile testing that the specific ultimate tensile strength (MPa/g) decreases as the infill percentage decreases and that hexagonal pattern infill geometry was stronger and stiffer than rectilinear infill. However, in finite element analysis, rectilinear infill showed less deformation than hexagonal infill when the same load was applied. Some design guidelines and future work are presented.

Commentary by Dr. Valentin Fuster
2016;():V002T01A016. doi:10.1115/MSEC2016-8754.

Origami-based sheet metal (OSM) folding techniques is a new emerging manufacturing procedure for sheet metal. In OSM the final part is folded into the desired 3D geometry using a sequence of folded bend lines. This process is enabled by creating material discontinues along the bend lines, either by laser cutting or by stamping. The objective of this paper is to optimize the design of OSM products while accounting for all possible flat patterns and accommodate manufacturing requirements for sheet metal products. OSM has an anticipated manufacturing benefits compared to traditional processes of sheet metal such as stamping; it requires minimal tooling and energy requirements thus is suitable for sustainable manufacturing alternatives. This paper discusses the implementation of optimization technique for OSM parts using a combination of traversal algorithm and manufacturing based indexes to reflect the requirements present in sheet metal industry. The outcomes of the optimization procedure resulted with topologically valid flat patterns with minimal scrap and wasted materials, in addition to minimal number of welded lines and fold line orientations in case of a robot effector is used to perform the fold. The work presented in this paper verified the validity of folding sheet metal using a single flat pattern into complex 3-D geometries from topological point view, in addition it highlights the major manufacturing concerns in folding sheet metal. This work also demonstrates a case study of optimizing a vehicular OSM part developed method.

Commentary by Dr. Valentin Fuster
2016;():V002T01A017. doi:10.1115/MSEC2016-8755.

Several bio-systems such as leaf veins, respiratory system, blood circulation, some plant xylem etc., involving multi-scale fractal topologies are being mimic for their inherent natural optimization. 3D fractal structures spanning multiple scales are difficult to fabricate. In this paper we demonstrate a new method to fabricate structures spanning meso and micro-scale in a relatively easy and inexpensive manner. A well known Saffman-Taylor instability is exploited for the same in a lifted Hele-Shaw cell. In this cell a thin layer of liquid is squeezed between two plates being lifted angularly leaving behind the fractal rearrangement of fluid which is proposed to be solidified later. We demonstrate and characterize fractal structures fabricated using two different fluids and corresponding methods of solidification. The first one is ceramic suspension in a photo-polymer and another is polystyrene solution with photo-polymerization and solvent vaporization as methods of solidification respectively. The fabrication process is completed in period of a few seconds.

Commentary by Dr. Valentin Fuster
2016;():V002T01A018. doi:10.1115/MSEC2016-8767.

We present a modular, reconfigurable system for building large structures. This system uses discrete lattice elements, called digital materials, to reversibly assemble ultralight structures that are 99.7% air and yet maintain sufficient specific stiffness for a variety of structural applications and loading scenarios. Design, manufacturing, and characterization of modular building blocks are described, including struts, nodes, joints, and build strategies. Simple case studies are shown using the same building blocks in three different scenarios: a bridge, a boat, and a shelter. Field implementation and demonstration is supplemented by experimental data and numerical simulation. A simplified approach for analyzing these structures is presented which shows good agreement with experimental results.

Commentary by Dr. Valentin Fuster
2016;():V002T01A019. doi:10.1115/MSEC2016-8837.

We propose metrics for evaluating the performance of robotically assembled discrete cellular lattice structures (referred to as digital materials) by defining a set of tools used to evaluate how the assembly system impacts the achievable performance objective of relative stiffness. We show that mass-specific stiffness can be described by the dependencies E*(γ, D(n, f, RA)), where E* is specific modulus, γ is lattice topology, and the allowable acceptance of the joint interface, D, is defined by an error budget analysis that incorporates the scale of the structure, and/or number of discrete components assembled, n, the type of robotic assembler, RA, and the static error contributions due to tolerance stack-up in the specified assembler structural loop, and the dynamic error limitations of the assembler operating at specified assembly rates, f. We refer to three primary physical robotic construction system topologies defined by the relationship between their configuration workspace, and the global configuration space: global robotic assembler (GR), mobile robotic assembler (MR), and relative robotic assemblers (RR), each exhibiting varying sensitivity to static, and dynamic error accumulation. Results of this analysis inform an iterative machine design process where final desired material performance is used to define robotic assembly system design parameters.

Topics: Robots , Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T01A020. doi:10.1115/MSEC2016-8849.

Cellular solids are a class of materials that have many interesting engineering applications, including ultralight structural materials [1]. The traditional method for analyzing these solids uses convex uniform polyhedral honeycombs to represent the geometry of the material [2], and this approach has carried over into the design of digital cellular solids [3]. However, the use of such honeycomb-derived lattices makes the problem of decomposing a three-dimensional lattice into a library of two-dimensional parts non-trivial. We introduce a method for generating periodic frameworks from Triply Periodic Minimal Surfaces (TPMS), which result in geometries that are easier to decompose into digital parts. Additionally, we perform multi-scale analysis of two cellular solids generated from two TPMS, the P- and D-Schwarz, and two cellular solids, the Kelvin and Octet honeycombs. We show that the simulated behavior of these TMPS-derived structures shows the expected modulus of the cellular solid scaling linearly with relative density, and matches the behavior of the octet truss.

Topics: Solids
Commentary by Dr. Valentin Fuster

Biomanufacturing: Advances in Additive Biomanufacturing of Tissues and Multiscale Engineered Tissue Constructs

2016;():V002T03A001. doi:10.1115/MSEC2016-8556.

The aim of this article is to investigate the effect of two different fixation hardware materials on bone remodeling after a mandibular reconstruction surgery and to restore the mandible’s function, healthy appearance, mastication, swallowing, breathing, and speech. The hypothesis is that using fixation hardware with stiffness close to that of the surrounding bone will result in a more successful healing process in the mandible bone.

The finite element model includes the material properties and forces of the cancellous bone, cortical bone, ligaments, muscles, and teeth. The reconstruction surgery is modeled by including the fixation hardware and the grafted bone. In the sectioned mandible, to best mimic the geometry of the mandible, two single barrel grafts are placed at the top of each other to form a double barrel graft set. Two different materials were used as the mandibular fixation parts, stiff Ti-6Al-4V, and porous superelastic Nickel-Titanium (NiTi) alloys.

A comparison of these two alloys demonstrates that using porous NiTi alloy as the fixation part results in a faster healing pace. Furthermore, the density distribution in the mandibular bone after the healing process is more similar to the normal mandible density distribution.

The simulations results indicate that the porous superelastic NiTi fixation hardware transfers and distributes the existing forces on the mandible bone more favorably. The probability of stress shielding and/or stress concentration decrease. This type of fixation hardware, therefore, is more appropriate for mandible bone reconstruction surgery.

Commentary by Dr. Valentin Fuster
2016;():V002T03A002. doi:10.1115/MSEC2016-8582.

Organ printing is an emerging technology for fabricating artificial tissues and organs, which are constructed layer by layer by precisely placing tissue spheroids or filaments as building blocks. These fabricated artificial organs offers a great potential as alternatives to replace the damaged human organs, providing a promising solution to solve organ donor shortage problem. Inkjetting, one of the key technologies in organ printing, has been widely developed for organ printing because of its moderate fabrication cost, good process controllability and scale-up potentials. Droplet formation process as the first step towards inkjetting 3D cellular structures needs to be studied and controlled precisely. This paper focuses on the ligament flow of exit-pinching during droplet formation process of inkjet printing. The ligament flow directions during pinch-off process of inkjet printing of a sodium alginate solution with a concentration of 0.5% (w/v) have been studied. It is found that two different types of flow directions inside a single ligament during pinch-off process may occur. At an excitation voltage of 30 V, the ligament flow has two different directions at the locations near the nozzle orifice and the jet front head: the negative z direction at the location near the nozzle orifice due to the dominant capillary effect, and the positive z direction at the location near the jet front head due to both the fluid inertial and capillary effects. On the contrary, at an excitation voltage of 70 V, the ligament flow directions are the same at the locations near the nozzle orifice and the jet front head: the positive z direction at the location near the nozzle orifice due to the sufficiently large fluid inertial effect, and the same positive z direction at the location near the jet front head due to both the fluid inertial and capillary effects. Two flow directions inside a single ligament benefit single droplet formation without satellite droplets, but the droplet trajectory will be easily affected by the airflow in the laboratory due to the small droplet velocity as well as the droplet deposition accuracy. Single flow direction inside a single ligament usually results in a long ligament due to the large fluid inertia which eventually breaks into several undesirable satellite droplets. The resulting knowledge will be beneficial for better understanding of the ligament pinch-off during droplet formation process of inkjet printing biological viscoelastic alginate bioink for 3D cellular structure fabrication as well as precise droplet controllability for good quality of fabricated 3D structures.

Topics: Flow (Dynamics)
Commentary by Dr. Valentin Fuster
2016;():V002T03A003. doi:10.1115/MSEC2016-8588.

Skin thermal burn wounds are classified by depth and require different levels of medical intervention. In this paper, the authors propose a novel treatment method where hyperspectral imaging (HSI) is applied to measure skin burn wound information that guide an additive biomanufacturing process to print a custom engineered skin graft in three dimensions (3D). Two dimensional principle component analysis (2DPCA) for noise reduction is applied to images captured by HSI in the visible wavelength range from 375 nm to 750 nm. A multivariate regression analysis is used to calculate hemodynamic biomarkers of skin burns, specifically the total hemoglobin concentration (tHb) and oxygen saturation (StO2) of the injured tissue. The biomarker results of the skin burn images are mapped spatially to show the burn wound depth distribution. Based on the biomarker values, the burn area is segmented into different sub areas with different burn degrees. Depth profiles of deep burns which require skin grafting are extracted from the burn distribution map. Next, each profile is processed to generate an additive biomanufacturing toolpath with a prescribed internal tissue scaffold structure. Using the toolpath, a 3D printer processes a custom graft from an alginate polymer hydrogel material. Alginate is chosen as the print material since it can be stretched into aligned fibers to create a porous structure that facilitates oxygen and nutrient uptake. The resultant printed construct demonstrates the feasibility of fabricating patient-specific tissues with custom-geometry grafts for treating clinical burns.

Commentary by Dr. Valentin Fuster
2016;():V002T03A004. doi:10.1115/MSEC2016-8619.

In the field of tissue engineering, scaffold is the foundation structure that provides the desired mechanical support for the tissue being engineered, surface for cells to attach and spread, and access for nutrient transport crucial for cell viability. The scaffolds are 3D building blocks which are designed and fabricated precisely prior to its implantation to the host tissue. When scaffolds with desired shape and size are fabricated, they can be seeded with cells and appropriate growth factors. After cells show healthy growth within the scaffold, they are implanted into the body with the scaffold to allow full-scale tissue regeneration.

In this research, photolithography is adapted as a fabrication method to generate PEGDA-based structures. In this method, ultra-violet (UV) light is reflected on PEGDA and as a result of the interaction between UV light and precursor solution, PEGDA turns into solid form.

Despite the potential of PEGDA in scaffold applications, the mechanical properties have not been studied in a great extent. Therefore, in this project, the mechanical characterization of PEGDA was conducted for various polymer concentrations. Specimens with 20%, 40%, 60%, 80% and 100% PEGDA to water ratio were prepared for compression tests. Our preliminary experimental data results show that, mechanical properties of PEGDA can be controlled by changing the PEGDA to water ratio. Stronger and stiffer structures can be obtained with high PEGDA concentrations while softer structures can be fabricated with reduced PEGDA concentrations.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T03A005. doi:10.1115/MSEC2016-8774.

With recent advancements in the direct electrostatic printing of highly viscous thermoplastic polymers onto an automated collector, melt electrospinning writing technology (MEW) has shown great potential for addressing the fundamental effects of an engineered scaffold’s dimensional parameters (e.g. fiber diameter, apparent pore size, and pore shape) on cultured cell–scaffold interactions. The superior resolution obtainable with MEW compared to conventional extrusion-based 3D printing technologies and its ability for toolpath-controlled fiber printing can facilitate the creation of a complex cell microenvironment or niche. Such a cell niche would provide the microscale fiber diameter and pore size for a scaffold substrate to present dimensional cues that affect downstream cellular function. In this study, the authors present in detail the design of a custom MEW system that allows simultaneous thermal management in the material, spin-line, and collector regimes using a heat gun. The complex interplay of process and instrument-based parameters is clarified with respect to stable jet formation allowing the printing of scaffolds with various microstructural patterned cues and consistent fiber diameter in a reproducible manner. Current fabrication of high fidelity scaffolds requires that the ratio of inter-fiber distance to fiber diameter to be an approximate value of 10. Since this manufacturing challenge yields pore sizes that are prohibitively large for 3D cell culture studies, particular emphasis is given in this paper to address the underlying physical mechanisms that will enable the fabrication of pore sizes with MEW scaffolds at cellular-relevant fiber diameters (10 – 50 μm). The authors show that appropriate toolpath planning that takes into account the different modes of the process can improve the inter-fiber distance resolution and thus the scaffold’s apparent pore size.

Commentary by Dr. Valentin Fuster
2016;():V002T03A006. doi:10.1115/MSEC2016-8787.

Stem cells are critical components of regenerative medicine therapy. However, the therapy will require millions to billions of therapeutic stem cells. To address the need, we have recently cultured stem cells in 3D microgels and used them as a vehicle for cell expansion within a low shear stress rotating wheel type bioreactor within a 500ml volumetric setting. This study specifically highlights the cell encapsulation in microbead process, harvesting and operation of microbeads within a dynamic bioreactor environment. We have specifically encapsulated stem cells (human adipose derived) into microbeads prepared from alginate hydrogels via an electrostatic jetting process. This study highlights the effect of fabrication process parameters on end-point biological quality measures such as stem cell count and viability. We were able to maintain a >80% viability during the 21 day static culture period. We have also measured the concentration of metabolites produced during the expansion, specifically lactate production measured during specific time points within culture inside the rotating wheel bioreactor Future work will need to address predicting yields in higher volume settings, efficiency of harvest and a more detailed description of the hydrodynamics affecting stem cell growth.

Commentary by Dr. Valentin Fuster

Biomanufacturing: Advances in Analysis, Design, and Manufacturing of Biomedical Devices

2016;():V002T03A007. doi:10.1115/MSEC2016-8513.

Due to the need of high speed and efficient biodosimetric assays for triage and therapy in the event of radiological or nuclear attack, a robotically-based automated biodosimetry tool (RABiT) has been developed over the past few years. Adapting the micronucleus assay from filter plates to V-shaped plates presented challenges in the liquid handling, namely cell splashing out of the V-shaped well plate during the cell harvesting, poor cell distribution on the bottom of the image plate during the dispensing, and cell loss from the image plate during the aspiration in the liquid handling process. Experimental and numerical investigations were carried out to better understand the phenomena and mitigate the problems. Surface tension and contact angle among the fluids and the plate wall were accounted for in the discrete and multiphase numerical models. Experimental conditions were optimized based on the numerical results showing the relationship between nozzle speed and amount of splashed liquid, and the relationship between aspiration speed and number of escaped cells. Using these optimized parameters, numbers of micronuclei in binucleated cells showed the same dose dependence in the RABiT-prepared samples as those in the manually prepared ones. Micronucleus assay protocol was fully realized on RABiT.

Commentary by Dr. Valentin Fuster
2016;():V002T03A008. doi:10.1115/MSEC2016-8690.

Tissue simulants are commonly used in medical procedure training and research to test the insertion and cutting mechanics of medical devices. Accurate representation of the forces and tissue properties is important for the efficacy of the training and research studies. This paper provides a quantitative method of determining the performance of tissue simulants. A force model was used to determine the three component forces: tearing, spreading, and friction forces, of a needle passing through six different skin simulants. Experiments were performed to determine the fracture toughness, shear modulus, friction between the simulant and needle, and the crack length in the simulant made by the needle. Polyurethane with Shore hardness 40 A was shown to be the best simulant by having the composition of forces most similar to porcine skin tissue: 39% and 61% for the tearing force, 18% and 32% for the spreading force, and 29% and 21% for the friction force for the polyurethane and porcine skin respectively.

Topics: Cutting , needles , Skin
Commentary by Dr. Valentin Fuster
2016;():V002T03A009. doi:10.1115/MSEC2016-8696.

Increasing biopsy length could reduce the false negative rate in prostate biopsy diagnosis. In this paper, the effect of magnetic abrasive finishing (MAF) of needle inner surface on inner friction force and biopsy length was investigated. The test was conducted using the tissue-mimicking material polyvinyl chloride (PVC) and chicken breast. The inner friction forces of MAF-polished and unpolished needles were measured. In the PVC phantom, the smoothly polished needle had lowest inner friction force (0.0024 N/mm) and longest biopsy sample (34.3 mm). Although the roughly polished needle had a smoother surface than the unpolished needle, the significance of the effects on the biopsy performance due to the slight improvement of the needle surface was not observed. The biopsy tests with chicken breast tissue showed the trends similar to the case with PVC phantom. The smoothly polished needle had the smallest inner friction force (0.024 N/mm) and the longest biopsy sample (9.6 mm). These results showed that lowering the friction force between the needle and tissue facilitates the sliding of tissue along the needle, leading to a long biopsy sample. MAF can reduce the friction at the needle-tissue interface and obtaining longer biopsy samples.

Topics: Friction , Finishing , needles
Commentary by Dr. Valentin Fuster
2016;():V002T03A010. doi:10.1115/MSEC2016-8731.

Bipolar forceps are a type of electrosurgical device (ESD) widely used for tissue welding in modern surgeries. ESDs have many advantages over traditional surgical tools including reduced blood loss, improved efficiency and lower surgeon fatigue. However, these devices suffer from tissue sticking and damage due to overheating which leads to poor tissue joint quality. The problem is potentially caused by uneven power distribution due to non-uniform compression applied by the bipolar forceps. In this study, the effect of compression force uniformity was investigated with an experimental setup to achieve a uniform and consistent compression force at the jaws of bipolar forceps. Comparative tissue welding experiments were conducted under both uniform and non-uniform compression force conditions with tissue mimicking material. In situ welding process parameters including compression force, electrical voltage, and current were collected and analyzed to understand the effect of compression force uniformity. Comparing the uniform to non-uniform compression force cases, the results indicate that tissue impedance is lower due to increased tissue contact area; the electrical power is initially higher during the first few milliseconds, but becomes lower for the rest of the welding process recorded. The experimental device developed in this study provides an important platform to understand the difference of tissue welding process under uniform and non-uniform compression force conditions.

Commentary by Dr. Valentin Fuster
2016;():V002T03A011. doi:10.1115/MSEC2016-8743.

This paper presents a unique design of solid surgical needle featured by its 4-plane bevel tip and shaft slots with the aim to further explore the potential of vibratory needle insertion for medical applications. The design philosophy of the needle was introduced. To overcome the challenging issues faced in fabricating the designed needles, a non-traditional manufacturing process using electric discharging machining (EDM) for the tip and slots is presented. Two important parameters for needle cutting edges, the inclination angle and the included angle, were derived from the two fabrication variables of the bevel angle and the interval angle. Needle prototypes of the proposed design were fabricated with different geometries, and they are used to conduct several different experiments. In the first experiment, the needles were inserted into tissue phantom, and the friction slope was chosen as the performance criterion. In the second experiment, the testing medium was skin-mimicking polyurethane sheet, and the puncture force and depth were used to evaluate the performance. In both experiments, different vibration conditions of frequency-amplitude combinations (250Hz-5μm, 250Hz-50μm and 1500Hz-5μm) were applied in terms of frequency and amplitude. The preliminary results showed both weakness and potentials of the proposed design, and indicated the necessity for more experiments. Experiments and results to validate the presented method are also presented. The design and manufacturing techniques presented in this paper can be used for the design and development of surgical needles and cutters for engineering and medical applications.

Commentary by Dr. Valentin Fuster
2016;():V002T03A012. doi:10.1115/MSEC2016-8794.

This study characterizes the forces in high-speed bone cutting and grinding for the use of haptic devices in surgical simulations. Unrealistic force feedback due to the lack of vibrational features is one of the most common drawbacks. Generally, the force profile can be decomposed to a mean force and a vibrational force magnitude. These forces are experimentally measured under various motions, including feed rate and tool orientation, to mimic manual operations and to understand the effects of these parameters. Change in feed rate was found to be insignificant in the overall force feedback, while the change in tool orientation showed statistically significant effects. The grinding burr and cutting burr also exhibited different forces under an identical condition. The explanation for the behavior of the forces based on the cutting and grinding conditions is discussed along with the results.

Commentary by Dr. Valentin Fuster
2016;():V002T03A013. doi:10.1115/MSEC2016-8801.

The objective of this study is to investigate smoothed particle hydrodynamics (SPH) method in simulating drilling process of both brittle and ductile materials. Drilling simulation is commonly performed by finite element method (FEM); however, it is challenging when applied to small debris generated by brittle materials or special cutting tools, due to the inability to capture small chip interactions. SPH was originally developed for flow analysis but has been recently used in cutting research. In this study, SPH is compared with FEM by four case studies. The results show that SPH can simulate ductile drilling, but the chip formation and forces are not as reasonable as FEM. On the other hand, SPH can capture small fragmented debris in brittle material drilling, which cannot be done by FEM with an equivalent mesh size. SPH method is also found to be affected by the distance between the particles (element size in FEM) and numerical errors on the free surfaces, both of which require further investigation beyond this paper.

Commentary by Dr. Valentin Fuster
2016;():V002T03A014. doi:10.1115/MSEC2016-8806.

Here we present the design and manufacture of a simulated middle cerebral artery for neurosurgeons to practice brain aneurysm surgical procedures. The middle cerebral artery made of silicone is hollow with anatomically accurate features (including the aneurysm) based on the patient geometry found via computed tomography. A five-step fabrication technique was developed to (1) 3D-print the positive shape of the middle cerebral artery, (2) submerge the vessel in silicone to create a mold of the negative shape of the vessel, (3) fill the silicone mold with low melting temperature metal to create a positive cast of the vessel, (4) coat the investment with silicone, and (5) remove the metal with hot water to create the hollow middle cerebral artery. This vessel has been evaluated by neurosurgeons and demonstrated to be feasible for brain aneurysm clinical simulations.

Commentary by Dr. Valentin Fuster
2016;():V002T03A015. doi:10.1115/MSEC2016-8840.

The most common method for mandibular reconstructive surgery is the use of a Ti-6Al-4V fixation device and a fibular double barrel graft. This highly stiff fixation hardware (E = 112 GPa) often shields the bone graft (E = 20 GPa) from carrying the load, which may result in bone resorption. Highly stiff Ti-6Al-4V fixation hardware is also likely to concentrate stress in the fixation plate or at screw threads, possibly leading to hardware cracking or screw pull-out. As a solution for that, we have proposed and studied the effect of using a low stiffness, porous NiTi fixation device [1–4]. Although the stress in the fixation device is increased, using such low stiffness fixation hardware, is preferable to have an even higher stress on the graft in order to minimize the risk of resorption or hardware failure. We assume that preloading screws allows them to better engage the fixation hardware with the plate and the surrounding bone and causes an increased von Mises stress. The fixation device can be patient-specific and additively manufactured, such that the shape would match the outer surface of the cortical bone.

In this study, we modeled a healthy cadaver mandible via CT-derived 3D surface data. The mandible was virtually resected in the molar region (M1−3). The model simulated the result of reconstructive surgery under the highest chewing loading regime (i.e., 526 N on first right molar tooth [5, 6]) where reconstruction was done with either Ti-6Al-4V fixation hardware or patient specific, stiffness-matched, porous NiTi fixation hardware. The calibration of the material properties for this simulation was done using experimentally obtained data (DSC and compression tests) of Ni-rich NiTi bulk samples. The analyzed term in the finite element analysis was stress distribution in the cortical and cancellous bone. Porous NiTi fixation devices were also produced using Selective Laser Melting (SLM) using the geometry of the aforementioned cadaver mandible.

In this paper we have studied the effect of additional torque or preload on the performance of the fixation plates. The finite element analysis demonstrated that applying a preload to the screws increased the stress on the bone. Under similar levels of applied preload, the porous NiTi fixation device showed an increased level of von Mises stress in the bone, particularly in the graft. Additionally, the analysis indicated the higher level of stress on the bone surrounding the screws for the case of using NiTi, which could contribute to increasing screw stability. The fabricated patient-specific fixation hardware not only matched the shape of cortical bone but also contained the level of porosity that defines the appropriate modulus of elasticity.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Advances in Cyber Physical Systems, Stochastic Modeling and Sensor Networks in Advanced Manufacturing

2016;():V002T04A001. doi:10.1115/MSEC2016-8660.

The task of cleaning surfaces where foreign particles are removed by mechanical scrubbing requires oscillatory motions of the cleaning tool. Selecting the optimal operation parameters is important to automate this task with robots. The operation parameters can be the tool speed, force applied to the surface, frequency and amplitude of tool oscillation, stiffness offered by the robot, etc. The optimal set of parameters will be different for different surface/stain profiles and physical limitations of the robot. A large number of cleaning experiments need to be done if we try to find the optimal parameters exhaustively in a high dimensional space. It will also take a significant number of experiments to find the right model for the cleaning function and predict the optimal cleaning parameters under supervised learning settings. Conducting large number of experiments is often not feasible. We describe a semi-supervised learning approach to reduce the number of cleaning experiments to automate the process of finding the optimal cleaning parameters for arbitrary surface/stain profiles. This generalized method is also applicable for the tasks of grinding and polishing. Results from experiments with two Kuka robots performing cleaning tasks show the validity of our approach.

Commentary by Dr. Valentin Fuster
2016;():V002T04A002. doi:10.1115/MSEC2016-8661.

In this paper, we address the complexities arising due to occlusions in robotic bin-picking. Our focus is on mixed-bins. Most traditional planners try to find collision-free paths to extract objects, while returning a failure whenever a collision is anticipated between the object to be extracted and a neighboring object occluding the former. We take a different approach in this paper. Our approach is inspired by the fact that simple motions of the grasped object may result in the transition of the object from a collision-state to a collision-free state. Our approach exploits the local geometric relationships between the objects in contact with each other and the change in these relationships as the grasped object is moved to make conservative predictions whether such motion results in tangle-free extraction. We demonstrate our approach using experiments with a Kuka LBR iiwa robot singulating objects from a pile of convex and concave objects.

Topics: Robotics
Commentary by Dr. Valentin Fuster
2016;():V002T04A003. doi:10.1115/MSEC2016-8663.

We present an approach to automatically learn a bimanual robotic cleaning task on compliant objects. One robot grasps the object, while the other robot cleans it. Given a part with unknown deformation characteristics, the system visually detects the regions to be cleaned, and generates plans for both the grasping and cleaning arms. As the system performs cleaning attempts and gains experience with multiple new parts, it learns models of the part deformation depending on the cleaning force and grasping parameters. A planner iteratively generates tool paths for both robots using the available knowledge to optimize the cleaning time, including (1) delays from regrasping a part to minimize deflection and (2) time taken for repeated cleaning attempts over regions that remained dirty. A nonparametric deflection model is learned separately for each part, with minimal assumptions of the material behavior. We demonstrate the approach on a system of two KUKA LWR iiwa robots and a set of thin planar parts. Results indicate that the system is effective at rapidly learning part deformation models to enable effective iterative cleaning performance.

Topics: Deformation , Robotics
Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Advances in Data Infrastructures for Manufacturing Operations Planning and Control

2016;():V002T04A004. doi:10.1115/MSEC2016-8635.

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization — and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times — each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control.

We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.

Commentary by Dr. Valentin Fuster
2016;():V002T04A005. doi:10.1115/MSEC2016-8666.

In recent years, sensor technology and data mining capabilities have advanced greatly, allowing advanced manufacturing enterprises to closely monitor their manufacturing operations. At the same time, a thriving market has developed for low cost consumer level sensors and processors. A proliferation of low cost sensing hardware, combined with the availability of free and open source software for performing data analytics, provides a new opportunity for smaller manufacturers. Yet, these tools have not been investigated deeply in the manufacturing world. In this work, we use a combination of low cost sensing hardware and free and open source software to monitor a milling machine operation. We demonstrate that the data collected from these sensors can be used to reliably determine the operating condition of the machine. These techniques will be very valuable for small manufacturers, to determine key factors such as machine utilization, or to detect catastrophic failures early during machining.

Topics: Machinery , Sensors , Milling
Commentary by Dr. Valentin Fuster
2016;():V002T04A006. doi:10.1115/MSEC2016-8751.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

Commentary by Dr. Valentin Fuster
2016;():V002T04A007. doi:10.1115/MSEC2016-8783.

The development of digital technologies for manufacturing has been challenged by the difficulty of navigating the breadth of new technologies available to industry. This difficulty is compounded by technologies developed without a good understanding of the capabilities and limitations of the manufacturing environment, especially within small-to-medium enterprises (SMEs). This paper describes industrial case studies conducted to identify the needs, priorities, and constraints of manufacturing SMEs in the areas of performance measurement, condition monitoring, diagnosis, and prognosis. These case studies focused on contract and original equipment manufacturers with less than 500 employees from several industrial sectors. Solution and equipment providers and National Institute of Standards and Technology (NIST) Hollings Manufacturing Extension Partnership (MEP) centers were also included. Each case study involved discussions with key shop-floor personnel as well as site visits with some participants. The case studies highlight SME’s strong need for access to appropriate data to better understand and plan manufacturing operations. They also help define industrially-relevant use cases in several areas of manufacturing operations, including scheduling support, maintenance planning, resource budgeting, and workforce augmentation.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Cloud Manufacturing: An Industrial Perspective

2016;():V002T04A008. doi:10.1115/MSEC2016-8530.

Cloud manufacturing (CMfg) aims to meet the customization demand of resource service demander (RSD) effectively. This article investigates the relationship between cloud service attributes and task completion from several aspects and a multi-dimensional classification scheme of cloud service attributes is established. Key service attributes of manufacturing cloud service are categorized in six aspects including role oriented, dynamic nature of data, steps of service composition, correlation between service attribute and fitness function of service composition, value types and dimension. From the perspective of attribute indexes, the relationship between service attributes and different demands of personalized customized are analysed and elaborated, and the corresponding objective functions are proposed.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A009. doi:10.1115/MSEC2016-8538.

To address the latest challenges in the service-oriented manufacturing paradigm, cloud manufacturing is proposed for not just large-scale enterprises, but also small- and medium-sized enterprises (SMEs). Although enterprises acknowledge its technical merits, many of them hold their actions until further proof. This paper studies specifically the market evolution of cloud manufacturing from an economic perspective. The anticipated development trend is presented based on game theory and equilibrium analysis. Three major stages, emerging, growing and maturing, are partitioned with interactions among cloud players, i.e. service provider, service consumer and service platform operator (INP). In each stage, both static and dynamic market behaviors are analyzed using classical economics models. Service providers and consumers with different levels of knowledge will act distinctly, this requires an INP to offer diverse strategies to match their profit expectations. The findings in this paper can help enterprises, particularly SMEs, to understand and evaluate their business strategies, and provide economic evidence that participating in shaping a “health” cloud manufacturing market is worthwhile.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A010. doi:10.1115/MSEC2016-8572.

Nowadays, many companies have decided to use other companies’ competencies and outsource part of their manufacturing and business processes to suppliers from abroad in order to reduce costs, improve quality of products, and offer better services to customers. On the other hand, this decision has faced organizations with new challenges. Organizations need to evaluate their supplier’s performance, and consider their weakness and strength to survive in high competitive markets.

In addition, cloud manufacturing, as a powerful tool will likely enable small and medium sized enterprises (SMES) to move towards using dynamic scalability and ‘free’ available data resources in a virtual manner and to provide solution-based, value-added, digital-driven manufacturing service over the Internet. The research presented in this paper aims to develop a supplier selection framework in the context of cloud manufacturing. The paper will present the background and concept of supplier selection following by proposed framework which consisted of four modules, multi-criteria module, bidding module, optimization module, and learning module. Finally, web based supplier selection approach will be briefly presented as it is not the main aim of this paper.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A011. doi:10.1115/MSEC2016-8581.

Advances in cloud-enabled service-oriented architectures (SOA) have caused a resurgence of industry interest in business process catalog as a vehicle for establishing shared references for collaborative business processes. With this paper, we start to explore the state of art and practice in business process catalog and classification scheme (BPCCS) development and use for the manufacturing industry. More specifically, this paper includes two major contributions. First, we identify a set of initial requirements for BPCCS. Second, we provide a use-case analysis based on the identified requirements. We end by comparing our BPCCS requirements with those being developed across other, BPCCS R&D groups.

Topics: Architecture
Commentary by Dr. Valentin Fuster
2016;():V002T04A012. doi:10.1115/MSEC2016-8620.

In recent years, the waste mobile phones are generated in large quantity in China. Those e-wastes gain more and more attention because of both the sharp increase in quantity and the recyclable resources they contain. Furthermore, the mobile phone recycling industry has experienced a trend of rapid growth as well. However, due to the lack of national policies and legislations, the recycling industry is now facing problems in recycling processes. Thus in this paper, mobile phone recycling industry in China is systematically analyzed and a Cloud-based approach is developed which integrates tracking, interaction and coordinator mechanism through the recycling processes. With the integration of various stakeholders, the system can provide integrated data system throughout the whole life cycle of the mobile phones for the policy maker, and provide guidance for the operations during recycling service for the recycling stakeholders.

Commentary by Dr. Valentin Fuster
2016;():V002T04A013. doi:10.1115/MSEC2016-8658.

The manufacturing of a product takes place in several partial steps and these mostly in different locations to save tax or to use the best providers. Therefore, in the era of Internet of Things (IoT) and modern Intelligent Production Environments (IPE) are going to be inevitably based on a cloud-based repository and distributed architecture to make data and information accessible everywhere as well as development processes and knowledge available for worldwide cooperation. Semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use can support Collaborative Adaptive Production Process Planning (CAPP) in a flexible, efficient, and effective way. Thus, semantic representations of such CAPP knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based knowledge repositories supporting CAPP scenarios as required for e.g., Small and Medium Enterprises (SMEs). When such contributors work together on a product component production planning, they exchange component production and manufacturing change information between different planning subsystems which require, e.g., a standardized product-feature- and production-machine feature representation. These data exchanges are mostly based on applying the already established Standard for the Exchange of Product model data (STEP) for the computer-interpretable representation and exchange of product manufacturing information. Furthermore, the planning process can be supported by so-called Function Block (FB) based knowledge representation models, serving as a high-level planning-process knowledge-resource template. Web-based and at the same time Cloud-based tool suites, which are based on process-oriented semantic knowledge-representation methodologies, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM) can satisfy the needs of representing such planning processes and their knowledge resources. In this way, WPIM can be used to support the integration and management of distributed CAPP knowledge, as well as its access and re-use in Manufacturing Change Management (MCM) including Assembly-, Logistics and Layout Planning (ALLP). Therefore, also a collaborative planning and optimization for mass production in a machine readable and integrated representation is possible. On the other hand, that knowledge can be shared within a cloud-based semantic knowledge repository. To integrate all these functionalities, this paper introduces a new method, called Knowledge-based Production Planning (KPP) and outlines the advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM). In this way, an enabling basis for achieving ALLP interoperability in Distributed Collaborative Manufacturing and Logistics will be demonstrated.

Commentary by Dr. Valentin Fuster
2016;():V002T04A014. doi:10.1115/MSEC2016-8669.

Although 3D printing has attracted remarkable attention from both industry and academia society, still only a relatively small number of people have access to required 3D printers and know how to use them. One of the challenges is that how to fill the gap between the unbalanced supply of various 3D printing capabilities and the customized demands from geographically distributed customers. The integration of 3D printing into cloud manufacturing may promote the development of future smart networks of virtual 3D printing cloud, and allow a new service-oriented 3D printing business model to achieve mass customization. This paper presents a primary 3D printing cloud model and an advanced 3D printing cloud model, and analyzes the 3D printing service delivery paradigms in the models. Further, the paper proposes a 3D printing cloud platform architecture design to support the advanced model. The proposed advanced 3D printing cloud model as well as the architecture design can provide a reference for the development of various 3D printing clouds.

Commentary by Dr. Valentin Fuster
2016;():V002T04A015. doi:10.1115/MSEC2016-8723.

Computer-aided manufacturing (CAM) software is used to develop a process plan, which consists of an operations list, tool paths, tooling, process parameters, and depending on the system, material handling operations. Upon completing the development of a process plan, setup sheets are generated for the personnel involved in the setup, production, testing, and product validation activities for a product. Typically, this documentation is in a hardcopy format, or is a static electronic document, and the direction of the communication is unidirectional — from the process planner to the support personnel. With the ubiquitous communications tools available to individuals today, a more sophisticated approach should be taken to transmit, store, and communicate changes to and from the shop floor. Presently, standard setup documentation consists of the project information utilized for the developed process plan. Pictures such as screen captures of the tool path, virtual verification images, and physical elements such as specialty tools may be included. However, modifications are made continuously to improve the cycle time, quality, or to adjust for other product or process changes. This research focuses on the development of interactive setup sheets that utilize existing desktop CAD/CAM software and mobile technologies, with the potential for leveraging the advantages of manufacturing cloud computing. Videos, links to additional documentation, and the ability to edit a subset of process parameters such as a tool diameter are incorporated. The operator is able to physically change tools or other key process setup information, and then send the information to the CAM system in order to regenerate the updated tool paths and documentation. Complementing the flexible, agile, and reconfigurable paradigms is the communication flexibility provided by fast wireless networks along with, cloud computing resources that can accessed with mobile devices, which are ubiquitous in today’s society. This technology that has not yet been heavily employed in the manufacturing environment, and research leveraging these new tools need to be explored.

Commentary by Dr. Valentin Fuster
2016;():V002T04A016. doi:10.1115/MSEC2016-8726.

The introduction of the Internet into the manufacturing environment is becoming a prominent trend. In this context, two important concepts concerning manufacturing, i.e. Industry 4.0 and cloud manufacturing have been proposed. Industry 4.0 refers to the fourth industrial revolution and is often understood as the application of Cyber-Physical Systems in industrial production with the help of the Internet to achieve the Internet of Things and the Internet of Services. Meanwhile, the Internet-based new business technology trends, such as cloud, servitization and collaboration, have brought about a novel cloud-based service-oriented manufacturing model — cloud manufacturing. These two concepts, though bearing some similarities, adopt different ideas and approaches for promoting the development of manufacturing industry. Given the great significance of the two concepts to the manufacturing industry, there is a need to understand their similarities and differences. This paper firstly gives a brief up-to-date review of Industry 4.0 and cloud manufacturing, and then clarifies the relationship between them based on the basic concepts and their current research statuses.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A017. doi:10.1115/MSEC2016-8733.

Cloud Robotics (CR) is the combination of Cloud Computing and Robotics, which encapsulate resources related with robots as services and is also the robotics’ next stage of development. Under this background, due to the characteristics of convenient access, resource sharing and lower costs, industrial cloud robotics (ICR) is proposed to integrate the industrial robots resources in the worldwide to provide ICR services in worldwide. ICR also plays an important role in improving the productivity of manufacturing. In the manufacturing field, Cloud Manufacturing (CM) and Sustainable Manufacturing (SM) is the developing orientation of future manufacturing industry. The energy consumption optimization of ICR is the crucial issue for manufacturing sustainability. However, currently, ICR systems are not programmed efficiently, which leads to the increase of production costs and pollutant emissions. Thus, it is an actual problem to optimize the energy consumption of ICR. In this paper, in order to achieve the goal of energy consumption optimization in worldwide range, the framework of ICR towards sustainable manufacturing is presented, as well as its enabling methodologies, and it is used to support energy consumption optimization services of ICR in the Cloud environment. This framework can be used to support energy-efficient services related with ICR to realize sustainable manufacturing in the worldwide range.

Commentary by Dr. Valentin Fuster
2016;():V002T04A018. doi:10.1115/MSEC2016-8752.

The emerging business technology trends such as cloud, Internet of Things, and the new requirements and challenges of future development of manufacturing industry such as green manufacturing and knowledge innovations, have together given rise to a novel cloud-based service-oriented manufacturing business model — cloud manufacturing. Since its inception, cloud manufacturing has attracted much attention of researchers from both academia and industry. As a nascent concept aiming to achieve comprehensive and full resource sharing and e-business collaboration, the success of cloud manufacturing depends heavily on the support and participation of industrial enterprises. However, so far there have been few reports on the status of cloud manufacturing among industrial enterprises. In order to understand the situation of cloud manufacturing in industry, we conducted a survey with respect to its acceptability and application prospect among enterprises located in Jiangsu province, China. This paper presents the results of the survey and some analysis.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A019. doi:10.1115/MSEC2016-8771.

The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing-as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemized virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment.

In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Engineering Analytics and Data Science for Advanced Manufacturing System Informatics and Sustainability

2016;():V002T04A020. doi:10.1115/MSEC2016-8506.

The productivity and efficiency of production systems are greatly influenced by their configuration and complex dynamics subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. In this paper, we develop a mathematical model of production systems with general structure (tandem line, parallel, and etc.) to estimate the status of the system (production counts and processing speeds of the stations, buffer levels and production loss) by using sensor data of disruption events. Real-time production system performance such as effective disruption events, opportunity window, and permanent production loss are identified, which is very useful in real-time control to increase overall system efficiency.

Commentary by Dr. Valentin Fuster
2016;():V002T04A021. doi:10.1115/MSEC2016-8518.

With the growing world demand on composite products, composite manufacturers are increasingly moving towards adopting automated composite manufacturing. However, current automated machines are not optimized and have throughput limitations. Most of them only target specific composite processes. Because the shortage of understanding on the processing factors and knowledge on precise process control, the products get high probability to come with defect. This paper provides an experimental methodology to explore the relationship between the structural properties of composite parts and processing factors. The paper also provides composite manufacturers a guide for processing condition optimization, productivity improvement and cost reduction. The raw material involved in this paper is an aerospace grade carbon fiber prepreg and was used to fabricate composite samples in a hot press machine system in the experiment. To investigate the effects of processing factors, specimens were fabricated using a series of combinations of different factor values. Three point bending flexural test was performed to evaluate the quality of each sample. Collected data are analyzed by statistical methods and fitted to a multiple linear regression model, which provides insights for composite manufacturing.

Commentary by Dr. Valentin Fuster
2016;():V002T04A022. doi:10.1115/MSEC2016-8617.

Bending is a fundamental manufacturing process to form sheet metals into intended angular geometries. Although the process has been extensively studied, predicting its accuracy is still challenging due to the Springback phenomenon inherent to the process. This research intends to combine bending and machining processes to improve bent workpiece angular dimensional accuracy. A minimum enclosing CAD model is first obtained by determining optimum thickness from the bend part CAD model to accommodate the estimated Springback in order to guide the selection of blank workpiece dimension for this bending/machining strategy. Then the machining areas are determined and the cutting forces are predicted to estimate the deformation in the machining process. Toolpath is planned on the surface profile considering both the cutter deflection and the incurred workpiece deformation during machining. This project aims to produce a bending part with the desired dimensional accuracy through a hybrid manufacturing approach. More importantly, it also provides a technological foundation to prototype angled parts at a low cost by avoiding high expenses in making new die.

Topics: Machining
Commentary by Dr. Valentin Fuster
2016;():V002T04A023. doi:10.1115/MSEC2016-8685.

To capture and forecast the volatility of customer needs, this paper proposes a forecast method within the framework of QFD (Quality Function Deployment), based on CTS (compositional time series) and VAR model (vector auto-regression model). The CTS formed by customer needs importance rating sampling within a period of time are treated as the basis to predict the future customer needs. Firstly, the CTS are transformed from the simplex space to the real domain. Then, the VAR model is established based on the time series obtained in the real domain. This model is used to accurately forecast beyond the sample and the predictive result is transformed back to the simplex space to obtain the predictive customer needs importance rating time series. Based on the predictive customer needs importance rating, the design attributes predictive priorities are calculated, which can guide the resources allocation in the development of personalized product, to provide better personalized product that is more in line with future customer needs. The case shows that the proposed method is effective.

Topics: Volatility
Commentary by Dr. Valentin Fuster
2016;():V002T04A024. doi:10.1115/MSEC2016-8704.

Increased demand on product variety entails a flexible assembly system for product families which can be quickly configured and reconfigured in a responsive manner to deal with various product designs. Development of such a responsive assembly system requires an in-depth understanding of the impact of product family design on assembly system performance. In this paper, the linkage between the product family design and assembly systems is characterized by an assembly hierarchy model, which reflects a hierarchical relationship among all possible sub-assemblies and components, assembly tasks, and material flow among the tasks. Our prior research developed a recursive algorithm to generate all assembly hierarchy candidates for one single product based on its liaison graph without redundancy. These generated assembly hierarchies provide a structure to help efficiently explore optimal assembly system designs with reduced computational load. In this paper, the application of the assembly hierarchy generation algorithm will be extended to a product family by developing joint liaison graph model. Taking the advantage of the modular design of the product family, we proposed a concept of multi-level joint liaison graphs to overcome the computational challenge brought by assembly hierarchy generation for joint liaisons. Two case studies were conducted to demonstrate the algorithm.

Topics: Manufacturing , Design
Commentary by Dr. Valentin Fuster
2016;():V002T04A025. doi:10.1115/MSEC2016-8717.

Controlling surface shape variations plays a key role in high-precision manufacturing. Most manufacturing plants rely on a number of multi-resolution measurements on manufactured surfaces to evaluate surface shapes and resultant quality. Conventional research on surface shape modeling focused on interpolation and extrapolation of spatial data using sampled measurements based on presumed spatial relationship over entire surface locations. However, the prediction accuracy is heavily restricted by the density of sampled measurements, preventing cost-effective evaluation of surface shape in high precision. New opportunities emerge for cost-effective high-precision surface manufacturing when the industry begins to extensively collect in-plant process information. This paper explores the opportunity by investigating strategies for fusing surface measurement data with multiple process variables. The fusion is achieved by characterizing the relationships between surface height and process variables using (1) linear regression based co-Kriging and (2) fuzzy if-then rules as well as considering spatial correlations. Under (3) Bayesian sequential updating frameworks, a generic surface variation model is updated sequentially using different process information. Case studies are conducted for comparisons and demonstrate the advantages of the fuzzy inference based spatial model.

Commentary by Dr. Valentin Fuster
2016;():V002T04A026. doi:10.1115/MSEC2016-8722.

A multistage system that consists of multiple stages for sequential operations to finish products is widely employed in modern manufacturing systems. Due to the characteristics of multistage systems, the product quality not only depends on operations in current stage but is also affected by operations in upstream stages. Most existing studies use Stream of Variation models to analyze error propagation and interactions among multiple stages in discrete manufacturing systems such as machining shops and assembly systems. In this paper, a multistage model based on the “Stream of Variation” concept is developed to investigate the tension propagation in a flexible material roll-to-roll manufacturing system. This modeling method uses a physical model coupled with a data-driven model to correlate the roller operation performance and product quality characteristics. Torque equilibrium analysis and Hooke’s law are employed for physical model and the censored regression model is used to explore unknown structures/parameters. A web unwinding process demonstrates the feasibility and prediction performance of the proposed model. The result shows that the proposed multistage model can serve as a virtual metrology method to increase system visibility, enhance health management capability and eventually improve product quality.

Commentary by Dr. Valentin Fuster
2016;():V002T04A027. doi:10.1115/MSEC2016-8735.

Effective maintenance operations are essential to improve the competitiveness of manufacturing enterprises. However, the existing maintenance policies usually ignore the real-time dynamics of the system and cannot respond promptly to the demand changes in the market. This paper investigates the hidden opportunities that one machine can be stopped for maintenance during production time, while the throughput requirement in a specific horizon can still be satisfied. We define these time windows as active maintenance opportunity windows (AMOWs), and predict them based on the real-time operational data in manufacturing systems with different configurations and Bernoulli machines.

Commentary by Dr. Valentin Fuster
2016;():V002T04A028. doi:10.1115/MSEC2016-8750.

Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. This paper develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

Commentary by Dr. Valentin Fuster
2016;():V002T04A029. doi:10.1115/MSEC2016-8757.

Leak tightness is one of the key quality characteristics of oil pipelines. In pipe connection processes, this quality characteristic is mainly characterized by two critical interpretable change points in the torque signals collected by sensors mounted on the connection machine. However, because of various noises from the operation and measurement systems, latent process factors, such as mechanical return difference, assembling misalignment, and straightness of pipes, cause various nonlinear patterns to exist in the torque signals. Hence, precisely identifying the change points for automatic quality examination is still challenging. In this paper, a two-stage modeling framework is proposed to utilize sequential change point detection to precisely locate the two critical change points. A two-phase regression model based on the F maximum test is employed to detect all potential change points in the first stage. Subsequently, a two-step backward change point selection algorithm based on mechanical principles is implemented to select the critical change points in the second stage. Finally, the change point selection based on a three-phase regression model is developed. The efficacy of the proposed framework is validated by a case study on a real threaded steel pipe connection process.

Topics: Steel , Pipes
Commentary by Dr. Valentin Fuster
2016;():V002T04A030. doi:10.1115/MSEC2016-8781.

With rapid advancements in sensing technologies and computation capabilities, high-resolution machine vision data have become available for various manufacturing processes. For machining, the use of machine vision data has shown great promise in machining tool condition monitoring, a critical factor for final product quality. Extensive research has been performed on wear characterization using intensity-based methods, but limited work has made use of process knowledge for image processing phases. Additionally, previous work focuses on single cutting edge machining tools, but no methods have been proposed for multiple cutting edge machining tools, such as broaches. In this paper, a process knowledge-based image filtering method is proposed to eliminate within-image and between-image noise to obtain effective wear region(s) for each cutting edge on a broach. In addition, these wear regions across multiple cutting edges are jointly described by fitting their relationship with each cutting edge’s respective chip load. Finally, the extracted model parameters are used for unsupervised learning to determine the entire tool’s degradation levels from a training dataset. A case study is introduced to show the effectiveness of the proposed methodology using a hexagonal broach.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: High Performance Computing and Data Analytics in Manufacturing

2016;():V002T04A031. doi:10.1115/MSEC2016-8525.

The lack of plug-and-play programmability in conventional toolpath planning approach in subtractive manufacturing, i.e., machining leads to significantly higher manufacturing cost for CNC based prototyping. In computer aided manufacturing (CAM) packages, typical B-rep or NURBS based representations of the CAD interfaces challenge core computations of tool trajectories generation process, such as, surface offsetting to be completely automated. In this work, the problem of efficient generation of free-form surface offsets is addressed with a novel volumetric representation. It presents an image filter based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The scalable voxel data structure and the proposed hardware-accelerated volumetric offsetting together advance the computation and memory efficiencies well beyond the capability of past studies. Additionally, in order to further accelerate the offset computation the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The accuracy of the offset algorithms is thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is robust in computation, easy to comprehend, and achieves a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing dual socket quad-core CPU implementation.

Commentary by Dr. Valentin Fuster
2016;():V002T04A032. doi:10.1115/MSEC2016-8559.

Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2016;():V002T04A033. doi:10.1115/MSEC2016-8625.

With rapid innovation in the electronics industry, product obsolescence forecasting has become increasingly important. More accurate obsolescence forecasting would have cost reduction effects in product design and part procurement over a product’s lifetime. Currently many obsolescence forecasting methods require manual input or perform market analysis on a part by part basis; practices that are not feasible for large bill of materials. In response, this paper introduces an obsolescence forecasting framework that is capable of being scaled to meet industry needs while remaining highly accurate. The framework utilizes machine learning to classify parts as active, in production, or obsolete and discontinued. This classification and labeling of parts can be useful in the design stage in part selection and during inventory management with evaluating the chance that suppliers might stop production. A case study utilizing the proposed framework is presented to demonstrate and validate the improved accuracy of obsolescence risk forecasting. As shown, the framework correctly identified active and obsolete products with an accuracy as high as 98.3%.

Topics: Machinery , Risk
Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Innovations in Equipment Design, Tooling, and Control/Automation to Enhance Manufacturing Processes

2016;():V002T04A034. doi:10.1115/MSEC2016-8529.

Conventional tool-paths for CNC (computer numerical controlled) machine tools or NC positioning systems are mainly composed of linear motion segments, or so called the G1 commands. Interpolating along linear tool-paths exhibits serious limitations in terms of achieving the desired part geometry and productivity in high-speed machining. Velocity and acceleration discontinuities occur at the junction points of consecutive segments. In order to generate smooth and continuous feed motion, a kinematic corner smoothing algorithm is proposed in this paper, which plans smooth acceleration and jerk profiles around the segment junction to realize continuous velocity transition between consecutive linear segments. The proposed corner-smoothing algorithm eliminates the need for geometry based corner-blending techniques and presents a computationally efficient interpolation scheme. The cornering error is controlled analytically allowing the end-user to control the cornering tolerance. Drive’s acceleration and the jerk limits are fully utilized to minimize overall cornering duration. This delivers a time optimal cornering motion within user specified cornering error tolerances. Simulation studies are used to demonstrate the effectiveness of proposed high-speed cornering scheme.

Commentary by Dr. Valentin Fuster
2016;():V002T04A035. doi:10.1115/MSEC2016-8573.

This paper presents smart tooling concepts applied to ultra-precision and high speed machining, particularly through the development of smart tool holders, two types of smart cutting tools and a smart spindle for high speed drilling and precision turning purposes respectively. The smart cutting tools presented are force-based devices, which allow measuring the cutting force in real time. By monitoring the cutting force a suitable sensor feedback signal can be captured, which can then be applied for the smart machining. Furthermore, an overview of recent research projects on smart spindle development is provided, demonstrating that signal feedback is very closely correlated to the drilling through a multilayer composite board. Implementation aspects on the proposed smart cutting tool are also explored in the application of hybrid dissimilar material machining.

Commentary by Dr. Valentin Fuster
2016;():V002T04A036. doi:10.1115/MSEC2016-8643.

This paper presents the design and characteristics of a new two-dimensional non-resonant tertiary motion generator which is based on the flextensional structure. A tool holder connects two perpendicularly-placed flextensional actuators with flexure hinges which decouple the motion outputs from the two actuators. Piezoelectric stacks, which are preloaded through precision screws, are used to provide input displacements. By balancing the requirements of driving current, stiffness, and the displacement amplification ratio, the proposed design is targeted to operate at above 10 kHz with two-dimensional vibrations amplitude of 10 μm in each directions. Technical difficulties in driving a non-resonant mode piezoelectric actuator at a high frequency are discussed. The solutions and optimization procedures are presented in this paper. The design is optimized by finite element simulation; and the results are presented.

Commentary by Dr. Valentin Fuster
2016;():V002T04A037. doi:10.1115/MSEC2016-8710.

Micro-structured surfaces have extensive applications in a wide array of fields, due to their improved functional performance. Existing manufacturing methods for these surfaces fall short of efficiency for volume production or are only applicable to a specific class of materials. In this paper, an innovative and highly-efficient machining method, elliptical vibration texturing (EVT), is proposed for rapid generation of micro-dimples on planar engineered surfaces. The cutting tool of the EVT process vibrates along an elliptical trajectory. The elliptical vibrations, when coupled with a high cutting velocity, impose micro-dimples onto workpiece surfaces while machining. The high productivity is achieved by adopting a newly designed tertiary motion generator which is able to deliver required elliptical vibrations at an ultrasonic frequency. The shape and distribution of the generated dimple patterns have been theoretically analyzed and predicted by a proposed simulation model. Preliminary texturing results using aluminum and brass as workpieces are given to validate the process principle and simulation model.

Topics: Vibration
Commentary by Dr. Valentin Fuster
2016;():V002T04A038. doi:10.1115/MSEC2016-8762.

In the painting process in automotive manufacturing, the repair polishing process is still done manually by a worker with a sufficient skilled technique. However, the number of skilled workers is decreasing with the aging. In addition, the polishing time and the surface quality after the repair polishing are dependent on the proficiency level of the worker. Thus, skill-independent automation technology for the repair polishing is required. In our past research, the serial-parallel mechanism polishing machine was developed for automating the polishing process. The developed machine can control the tool trajectory, tool posture and polishing force simultaneously. In addition, the polishing force is controlled without external sensors by the reaction force observer system. This study aims to develop a polishing automation method for unknown 3-dimensional curved surface by using the developed machine. First, the tool posture control method on unknown curved surface was proposed. Second, the normal force control method based on the posture information was proposed. By using these proposed methods simultaneously, the tool posture and polishing force were controlled in the normal direction on unknown 3-dimesional curved surface. From the experimental results, the validity of the proposed method was verified.

Commentary by Dr. Valentin Fuster
2016;():V002T04A039. doi:10.1115/MSEC2016-8862.

Bi-rotary milling head is one of the core components of five-axis machining center and its dynamic characteristics directly affect the machining stability and accuracy. During the five-axis machining, the milling head would change the posture continuously. To facilitate rapid evaluation and optimization of the dynamic behavior of the bi-rotary milling head within the whole workspace, a multi-rigid-body dynamic model considering the flexible joint is established. The varying stiffness of the flexible joints affected by gravity and cutting force at different swing angles is analyzed. A parametric dynamic equation with posture parameters and physical parameters is finally deduced. The sensitive structural parameters on natural frequency and its fluctuation within the workspace are obtained by the sensitivity analysis. The theoretical and experimental results show that the dynamics of the bi-rotary milling head behave pose-dependent and the varying dynamics can be positive controlled by modifying sensitive structural parameters at the design stage.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Intelligent Maintenance Decision Making for Manufacturing Systems

2016;():V002T04A040. doi:10.1115/MSEC2016-8547.

In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the shaving process. Because shaving tool condition monitoring is not well-studied, we extract both traditional and novel features from accelerometer signals collected from the shaving machine. We then apply a heuristic feature selection technique to identify key features and classify the tool condition. Run-to-life data from a shop-floor application is used to validate the proposed technique.

Commentary by Dr. Valentin Fuster
2016;():V002T04A041. doi:10.1115/MSEC2016-8615.

The maintenance in manufacturing systems has been widely studied to improve equipment reliability, increase the productivity, and reduce operational cost. Recently, with the increasing concerns on climate change and environmental protection, the energy related performance of manufacturing system has also draw wide attention from both academia and industry. One common decision of maintenance and energy management can be the identification of the machines that could be shut down in the manufacturing system for either maintenance or energy saving purpose. Thus, the idea of implementing maintenance and energy control simultaneously has emerged. Some existing cases about the joint maintenance and energy control in manufacturing systems can be found in literature. In this paper, we further analyze the existing opportunities for joint energy and maintenance decision making in manufacturing systems. A few research directions towards this goal are proposed and discussed. The corresponding research challenges are also analyzed. A numerical case based on a section of an automotive assembly line is used to illustrate the potential benefits of the proposed approach.

Commentary by Dr. Valentin Fuster
2016;():V002T04A042. doi:10.1115/MSEC2016-8674.

Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of dimensional precision and accuracy. The efficiency of powder fusion process in powder-bed AM processes is highly affected by process errors, powder irregularities as well as geometric factors. Formation of defects such as lack of fusion and over-fusion due to the aforementioned factors causes dimensional errors that significantly damage the precision.

This paper addresses the development of an automated in-situ inspection system for powder-bed additive manufacturing processes based on machine vision. The results of the in-situ automated inspection of dimensional accuracy allows for early identification of faulty parts or alternatively in-situ correction of geometric errors by taking appropriate corrective actions. In this inspection system, 2D optical images captured from each layer of the AM part during the build are analyzed and the geometric errors and defects impairing the dimensional accuracy are detected in each layer. To successfully detect geometric errors, fused geometric objects must be detected in the powder layer. Image processing algorithms are effectively designed to detect the geometric objects from images of low contrast captured during the build inside the chamber. The developed algorithms are implemented to a large number of test images and their performance and precision are evaluated quantitatively. The failure probabilities for the algorithms are also determined statistically.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Process-Machine-Interactions (PMI) in Advanced Manufacturing

2016;():V002T04A043. doi:10.1115/MSEC2016-8665.

Coupled torsional-axial vibrations of the drilling tool play a significant role in the machining dynamics of the drilling process. In this paper, the torsional-axial vibrations of the drilling tool due to the warping deformation of the pretwisted flute is modeled. An enhanced receptance coupling model is developed to predict the coupled torsional-axial vibrations of the drilling tool considering the dynamics of fixture and clamping conditions. Rigid and flexible receptance coupling methods are used, with the simulation results verified through modal experiments. The proposed model is able to provide optimum drilling tool configurations to avoid undesired tool vibrations and improve hole quality.

Commentary by Dr. Valentin Fuster
2016;():V002T04A044. doi:10.1115/MSEC2016-8713.

This is Part I of a two part series study on mechanical ruling of diffraction grating. Mechanical ruling is a major method for fabricating diffraction grating. In mechanical ruling, the first step is to prepare the Al film. High quality preparation technique is needed to satisfy the requirements of the thickness and the mechanical properties of the film. The purpose of this paper is to present a complete study on the preparation technique and mechanical properties of the Al film used for mechanical ruling of diffraction grating. XRD and SEM experiments and analysis were conducted to investigate the microstructure of the Al film and the mechanical properties of the Al film were measured using nanoindentation and scratch tests. The Al film exhibits favorable mechanical properties which will the key for the experimental and numerical simulation studies of mechanical ruling of diffraction grating.

Commentary by Dr. Valentin Fuster
2016;():V002T04A045. doi:10.1115/MSEC2016-8715.

This is Part II of a two-part series study on mechanical ruling of diffraction grating. In Part I, effects of the Al film preparation process on the film attributes were investigated in terms of surface morphology, microstructure and mechanical properties using SEM, XRD, nanoindentation and scratch tests. In this part, mechanical ruling experiments were carried out on the prepared Al films with various thicknesses. The effect of ruling loads on the groove geometry were investigated. The tool wear after the mechanical ruling tests was inspected. A three-dimensional thermomechanical coupled finite element (FE) model was developed to predict the deformation and temperature fields during mechanical ruling. The strain gradient plasticity model was used in the FE analysis to model the size effect during the process. The multi-pass effect on the variation of groove geometry was predicted and analyzed with the FE model under different loading conditions.

Commentary by Dr. Valentin Fuster
2016;():V002T04A046. doi:10.1115/MSEC2016-8857.

Retroreflectors (RR) represent optical elements whose primary functionality is to return to the incident light back to its originating source. While inverted cube-corner (ICC) geometry constitutes de facto standard in automotive lighting applications, other RR designs exist. Among them, right triangular prism (RTP) constitutes a viable alternative and therefore, the main intention of the present study was to demonstrate a fabrication means other than the ineffective conventional pin-bundling technology are possible.

To address this, a new ultraprecise single point inverted cutting (SPIC) technology — envisioned as a virtual combination between diamond turning and five-axis machining — was introduced as a viable manufacturing option for the fabrication of the RTP RR arrays. While simulation results seem to suggest a slight optical superiority of the RTP RR arrays produced through conventional, rather than SPIC approaches, experimental results have demonstrated that fabricating RTP RR prototypes is not only possible, but it can yield better retroreflective efficiencies when compared to state-of-the-art ICC-based automotive retroreflectors.

Commentary by Dr. Valentin Fuster
2016;():V002T04A047. doi:10.1115/MSEC2016-8872.

This study presents a new method to identify parameters representing cutting process and transfer function of flexible mechanical structures mounted on a traveling stage by utilizing only internal information of computerized numerical control (CNC) system. Disturbance force input to CNC is estimated by disturbance observer and cutting force is estimated based on cutting force model. Analyzing influence of the estimated cutting force on the disturbance force, parameters used in the assumed models of cutting process and structural dynamics are identified in quasi-real-time. Least square method (LSM) is utilized for the parameter identification. Face turning experiment using an ultra-precision machine tool was conducted to verify feasibility of the proposed method. Experimental results clarified that the cutting force coefficient and the modal parameters representing the dynamic characteristics of the force transfer function can be identified accurately by the proposed method.

Commentary by Dr. Valentin Fuster

Properties, Applications and Systems: Quality Assurance in Additive Manufacturing Systems: Integrated Sensing, Modeling and Control

2016;():V002T04A048. doi:10.1115/MSEC2016-8516.

The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-val. < 1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (such as mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, and thus proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of AM components, and thus encourage wider adoption of AM capabilities.

Commentary by Dr. Valentin Fuster
2016;():V002T04A049. doi:10.1115/MSEC2016-8535.

The aim of this paper is to demonstrate a pathway for in situ real-time monitoring and closed-loop control of aerosol jet printing (AJP) process. To achieve this aim, we instrumented an Optomec AJ-300 aerosol jet printer with multiple temporal and image-based sensors. Experiments were conducted by varying the sheath gas flow rate (ShGFR) and, subsequently, the line morphology was acquired online using a CCD camera mounted coaxial to the nozzle (perpendicular to the platen). To assess the line morphology, we devised a novel digital image processing method that quantifies aspects of line morphology, such as line density, overspray, continuity, edge smoothness, etc. As a result, an optimal process window was established. Next, the underlying aerodynamic phenomena that influence the line morphology are explained based on a two dimensional computational fluid dynamics (2D-CFD) model. Thus, the image processing approach proposed in this work can be used to detect incipient process drifts, while the CFD model will be valuable to suggest the appropriate corrective action to bring the process back in control. We further validate that there is a good agreement between the online and offline results with respect to the quantified morphology of the lines.

Commentary by Dr. Valentin Fuster
2016;():V002T04A050. doi:10.1115/MSEC2016-8815.

Shape deformation is an important issue in additive manufacturing (AM) processes such as the projection-based Stereolithography. Volumetric shrinkage and thermal cooling during the photopolymerization process combined with other factors such as the layer-constrained building process lead to complex deformation that is difficult to predict and control. In this paper, a general reverse compensation method and related computation framework are presented to reduce the shape deformation of AM fabricated parts. During the reverse compensation process, the shape deformation is calculated based on physical measurements of shape deformation. A novel method for identifying the correspondence between the deformed shape and the given nominal computer-aided design (CAD) model is presented based on added markers. Accordingly, a new CAD model based on the shape deformation and related compensation is computed. The intelligently revised CAD model by going through the same building process can result in a fabricated part that is close to the nominal CAD model. Two test cases have been designed to demonstrate the effectiveness of the presented method and the related computation framework. The shape deformation in terms of L2- and L-norm based on measuring the geometric errors is reduced by 40–60%.

Commentary by Dr. Valentin Fuster

Sustainable Manufacturing: Advances in Data Observation Through System-of-Systems Integration Across the Product Lifecycle

2016;():V002T05A001. doi:10.1115/MSEC2016-8677.

Cellulosic ethanol can be used as a sustainable alternative transportation fuel. A major obstacle to restrict large-scale cellulosic ethanol manufacturing is low bulk density of cellulosic biomass that increases costs during transportation, storage and application of biomass. Biomass pelleting can significantly increase density of biomass. Pellets with high density can be handled, transported, stored and utilized easily. Ring-die pelleting, a traditional pelleting method, is widely used in biomass densification industry. This paper reported an experimental study to compare pellet quality (such as equilibrium moisture content, density, and durability) and microstructure of corn stover processed by a ring-die pellet mill with three different die sizes. Results showed that round and bigger die (32 mm instead of 9 mm) results in higher pellet density, durability, and lower equilibrium moisture content.

Commentary by Dr. Valentin Fuster
2016;():V002T05A002. doi:10.1115/MSEC2016-8702.

The manufacturing industry is evolving and starting to use 3D models as the central knowledge artifact for product data and product definition, or what is known as Model-based Definition (MBD). The Model-based Enterprise (MBE) uses MBD as a way to transition away from using traditional paper-based drawings and documentation. As MBD grows in popularity, it is imperative to understand what information is needed in the transition from drawings to models so that models represent all the relevant information needed for processes to continue efficiently. Finding this information can help define what data is common amongst different models in different stages of the lifecycle, which could help establish a Common Information Model. The Common Information Model is a source that contains common information from domain specific elements amongst different aspects of the lifecycle. To help establish this Common Information Model, information about how models are used in industry within different workflows needs to be understood. To retrieve this information, a survey mechanism was administered to industry professionals from various sectors. Based on the results of the survey a Common Information Model could not be established. However, the results gave great insight that will help in further investigation of the Common Information Model.

Commentary by Dr. Valentin Fuster
2016;():V002T05A003. doi:10.1115/MSEC2016-8792.

Advances in information technology triggered a digital revolution that holds promise of reduced costs, improved productivity, and higher quality. To ride this wave of innovation, manufacturing enterprises are changing how product definitions are communicated — from paper to models. To achieve industry’s vision of the Model-Based Enterprise (MBE), the MBE strategy must include model-based data interoperability from design to manufacturing and quality in the supply chain. The Model-Based Definition (MBD) is created by the original equipment manufacturer (OEM) using Computer-Aided Design (CAD) tools. This information is then shared with the supplier so that they can manufacture and inspect the physical parts. Today, suppliers predominantly use Computer-Aided Manufacturing (CAM) and Coordinate Measuring Machine (CMM) models for these tasks. Traditionally, the OEM has provided design data to the supplier in the form of two-dimensional (2D) drawings, but may also include a three-dimensional (3D)-shape-geometry model, often in a standards-based format such as ISO 10303-203:2011 (STEP AP203). The supplier then creates the respective CAM and CMM models and machine programs to produce and inspect the parts. In the MBE vision for model-based data exchange, the CAD model must include product-and-manufacturing information (PMI) in addition to the shape geometry. Today’s CAD tools can generate models with embedded PMI. And, with the emergence of STEP AP242, a standards-based model with embedded PMI can now be shared downstream.

The on-going research detailed in this paper seeks to investigate three concepts. First, that the ability to utilize a STEP AP242 model with embedded PMI for CAD-to-CAM and CAD-to-CMM data exchange is possible and valuable to the overall goal of a more efficient process. Second, the research identifies gaps in tools, standards, and processes that inhibit industry’s ability to cost-effectively achieve model-based-data interoperability in the pursuit of the MBE vision. Finally, it also seeks to explore the interaction between CAD and CMM processes and determine if the concept of feedback from CAM and CMM back to CAD is feasible. The main goal of our study is to test the hypothesis that model-based-data interoperability from CAD-to-CAM and CAD-to-CMM is feasible through standards-based integration. This paper presents several barriers to model-based-data interoperability. Overall, the project team demonstrated the exchange of product definition data between CAD, CAM, and CMM systems using standards-based methods. While gaps in standards coverage were identified, the gaps should not stop industry’s progress toward MBE. The results of our study provide evidence in support of an open-standards method to model-based-data interoperability, which would provide maximum value and impact to industry.

Commentary by Dr. Valentin Fuster

Sustainable Manufacturing: Advances in Sustainable Manufacturing Processes and Systems

2016;():V002T05A004. doi:10.1115/MSEC2016-8618.

3D printing has been recognized as an efficient and sustainable technology in the fields of advanced manufacturing. In the past few years, a considerable research, including basic theoretical research, technology innovation and industries application, have been conducted to promote 3D printing for a better performance in manufacturing. However, the benefits of 3D printing from environmental perspective are still to be seen and its sustainability is also a mystery. This paper presents a critical review about the qualitative and quantitative environmental impact of 3D printing to provide a comprehensive understanding of 3D printing for the public and provide a better guide for the future research. In addition, based on the principle of multi-objective optimization, this paper proposes a novel framework for 3D printing processes sustainability assessment and improvement by integrating the product Computer Aided Design (CAD) and Life Cycle Assessment (LCA). At last, recommendations about major concerns when analyzing the sustainability of 3D printing are put forward, which might be considered for the coming research.

Commentary by Dr. Valentin Fuster
2016;():V002T05A005. doi:10.1115/MSEC2016-8744.

Increasing manufacturing complexity continues to be one of the most significant challenges facing the manufacturing industry today. Due to these rapid changes in manufacturing systems, one of the most important factors affecting production is recognized as the frequent production setup or changeovers, consequently affecting the overall production lead times and competitiveness of the company. Developing responsive production setup and process capability is increasingly important as product ranges and varieties in manufacturing companies are growing rapidly and, at the same time, production business models are operating more towards being customer-oriented. Furthermore, although different conventional methods have been used to manage complexity in production changeovers, sustainability and competitiveness development in a manufacturing company needs to be scientifically addressed by managing manufacturing complexity. In this paper, a sustainable manufacturing-oriented approach is presented in mind of managing manufacturing changeover complexities. A case study is carried out specifically concerning changeover complexity in a pharmaceutical company, aiming at minimizing complexities in production changeover and waste, increasing plant flexibility and productivity, and ultimately the sustainable competitiveness of the company in managing manufacturing changes.

Commentary by Dr. Valentin Fuster
2016;():V002T05A006. doi:10.1115/MSEC2016-8748.

This research experimentally investigates the characteristics of micro end-milling process of titanium alloy using nanofluid minimum quantity lubrication (MQL) with chilly CO2 gas. In the nanofluid MQL, hexagonal boron nitride (hBN) particles having a lamellar structure are used. They have high aspect ratio and enable sliding against other particles, which can provide better lubricity. In addition, the chilly CO2 gas enhances a cooling effect during the micro end-milling process. A series of micro end-milling experiments are conducted in the meso-scale machine tool system, and milling force, coefficient of friction, surface roughness and tool wear are observed and analyzed according to varying lubrication and cooling conditions. The results show that the nanofluid MQL with chilly gas can be effective for reducing milling forces, coefficient of friction, tool wear and improving surface quality.

Commentary by Dr. Valentin Fuster

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