ASME Conference Presenter Attendance Policy and Archival Proceedings

2017;():V001T00A001. doi:10.1115/DETC2017-NS1.

This online compilation of papers from the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2017) 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presenter attendance policy, if a paper is not presented at the Conference by an author of the paper, the paper will not be published in the official archival Proceedings, which are registered with the Library of Congress and are submitted for abstracting and indexing. The paper also will not be published in The ASME Digital Collection and may not be cited as a published paper.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

37th Computers and Information in Engineering Conference: Advanced Modeling and Simulation

2017;():V001T02A001. doi:10.1115/DETC2017-67589.

Hyperelastic material model parameters have been developed to capture the behavior of silicone based construction sealants. Modern commercially available finite element analysis software makes it quite accessible to develop hyperelastic material models, automating the process of curve-fitting experimental lab data to specific hyperelastic formulations. However, the process of selecting a particular hyperelastic model from those supported is not straightforward. Here, a series of lab experiments are employed to guide the selection of the hyperelastic model that best describes various structural silicone glazings. A total of 10 different sealants are characterized with discussion of variations among the models. Comparisons of the best performing hyperelastic models for the different sealants are presented. Finally, an application is described in which these hyperelastic models have begun to be implemented in practice.

Commentary by Dr. Valentin Fuster
2017;():V001T02A002. doi:10.1115/DETC2017-67593.

A computational study is conducted to evaluate the performance of an extraterrestrial submarine operating in the liquid hydrocarbon seas of Saturn’s largest moon, Titan. To simulate the flow around the submarine and offer a prediction for thrust and power requirements, Computational Fluid Dynamics tools, ANSYS© FLUENT© and DualSPHysics, are utilized for the deeply submerged and near-surface conditions, respectively. Several operational scenarios are investigated and comparisons are made with other available results with a good qualitative and quantitative agreement.

Commentary by Dr. Valentin Fuster
2017;():V001T02A003. doi:10.1115/DETC2017-67811.

The present work introduces the motivation, architecture and preliminary analytical and computational framework that will eventually lead to aging predictions of cathodic surfaces along with its implications on impressed current cathodic protection (ICCP) systems. This is necessary for adjusting ICCP systems in a manner that reflects the dissipative nature of cathodic surface assemblies while at the same time enabling potential electric far field requirements. We describe various approaches for developing Cathodic Surface Aging Models (CSAMs) based on both data-driven and first principles based methodologies. A computational ICCP framework is implemented to account for cathodic aging in a manner that allows the utilization of various CSAMs. An application of this framework demonstrates the applicability of the implications of the variability of the polarization curves as it is associated with cathodic surface aging. In addition to a data-driven CSAM based on a loft-surface approximation we also introduce a first principles thermodynamic theory for aging and the design of a systematic experimental task for validating and calibrating this theory.

Commentary by Dr. Valentin Fuster
2017;():V001T02A004. doi:10.1115/DETC2017-67831.

A popular, but unsubstantiated view is that tree branch morphologies are similar (self-similarity) and are of an iterative nature. To date there are no studies that document plant branch self-similarities. The purpose of this research is to develop a program (3D Simquant) that estimated self-similarities among paired branch terminals quantitatively. After 3D Simquant was written, the program was verified and sensitivity analysis performed, eighty-five terminal branch pair-wise comparisons from five different tree species were analyzed. Only two branch geometries (Y and Y+1 terminals) were compared. Simple Y terminals are terminal main stems with one side branch while Y+1 terminals are main steams with two side branches. Similarities among paired branch terminals were quantified with Root-Mean-Square-Error (RMSE) after registration of images. For the five species tested, all Y terminals had RMSE values less than 1.5 which indicates they were similar. For most Y+1 terminals, RMSE values were twice that of Y terminals indicating the Y+1 samples were more dissimilar than Y terminals. Overall, the programs were accurate and rapid for an analysis of branch similarities.

Commentary by Dr. Valentin Fuster
2017;():V001T02A005. doi:10.1115/DETC2017-68044.

Recently there has been increased interest in developing finite element modeling schemes where the geometry is independent of the mesh and is immersed in a uniform structured background mesh. The main motivation of this approach has been to avoid the difficulties of generating a conforming mesh for complex geometry. For this type of mesh independent analysis, modeling of assemblies, composite microstructures, and interaction at the interfaces is studied. In this paper, we present step boundary method as a well-suited method for applying essential boundary conditions, contact at interfaces as well as imposing periodic boundary conditions. This method uses step functions to construct trial solutions that satisfy boundary conditions as well as interface contact conditions and periodic boundary conditions. A generalized version of this approach is presented that allows arbitrary coordinate system for imposing boundary conditions and permits contact with sliding. Several test examples are used to validate this approach.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Computer-Aided Product and Process Development

2017;():V001T02A006. doi:10.1115/DETC2017-67031.

The algorithm-based product development process applies mathematical optimization tools in the conceptual steps of the product development process. It relies on formalized data such as initial loads and boundary conditions to find the best product solution for optimized bifurcated sheet metal parts. Previous research efforts focused on the automation of CAD modeling steps. Current algorithms are able to generate the CAD models of optimized bifurcated sheet metal products automatically, however, they are rough with low-level of detail and abstraction. Consequently, CAD models are embodied and detailed manually in a partly iterative and time-consuming process to include parameters, constraints and design features. Hence, this paper introduces feature recognition and parametrization methods for the algorithm-based product development of bifurcated sheet metal products. It proposes the inclusion of a pre-processor to analyze the solution graph resulted from topology optimization before the generation of CAD models. Algorithms that derive the geometric shape from the solution graph by recognizing features as well as assigning parameters are introduced. Then, feature-based CAD models of bifurcated sheet metal products are automatically generated. The proposed methods and algorithms are implemented with Python and validated with a use-case. Benefits and limitations of the proposed methods are discussed.

Commentary by Dr. Valentin Fuster
2017;():V001T02A007. doi:10.1115/DETC2017-67227.

The inevitable presence of geometrical part deviations and their effects on the product function and quality forces companies to manage these deviations along the product life-cycle. Computer-aided tolerancing and variation simulation tools support these activities by enabling the early prediction and assessment of the effects of such part deviations. However, available approaches for the tolerance analysis and variation simulation imply shortcomings regarding the consideration of form deviations, particularly when analysing mechanism and moving systems. This paper presents an approach for the motion tolerancing, i. e. for the tolerance analysis of mechanism and moving systems, employing discrete geometry representations of parts with geometrical deviations (Skin Model Shapes). In this regard, a novel method for the parametrization of form deviations is proposed and the use of meta-modelling techniques for the reduction of the computational effort is illustrated. Moreover, the presented approach is applied on two case studies. Based on the obtained results, it can be found, that the consideration of form deviations in the tolerance analysis for mechanism and moving systems may lead to more purposeful tolerancing decisions.

Topics: Modeling , Shapes , Skin
Commentary by Dr. Valentin Fuster
2017;():V001T02A008. doi:10.1115/DETC2017-67503.

3D printed electronics introduces new opportunities in product design. On the other side, it also brings challenges regarding many aspects in the design and manufacturing process of prototypes/products. In this paper, based on our preliminary investigations, we summarize the opportunities of using 3D printed electronics in product design as: 1) it offers designers more freedom in their designs; 2) it promotes miniaturization of design; 3) it accelerates the design and manufacturing process; 4) it is able to improve the efficiency of producing customized/personalized mechatronic/electronic products and 5) it improves the sustainability in the product design and manufacturing process. Motivated by those opportunities, we conducted four case studies regarding four key aspects of 3D printed electronics: the conductive materials, geometric modelling, multiphysics simulation and manufacturing tools. Based on the findings in those case studies, we identified the challenges in 3D printed electronics and highlight the future works which may provide a better support to the needs of product designers.

Commentary by Dr. Valentin Fuster
2017;():V001T02A009. doi:10.1115/DETC2017-67564.

A robust method for surface fitting in 3D point cloud is presented as an application of the robust estimation of multiple in-lier structures algorithm [1]. The geometric primitives such as planes, spheres and cylinders are detected from the point samples in the noisy dataset, without regenerating surface normals or mesh. The inlier points of different surfaces are classified and segmented, with the tolerance of error for each surface estimated adaptively from the input data. From the segmented points, designers can interact with the geometric primitives conveniently. Direct modification of 3D point cloud and inverse design of solid model can be applied. Both synthetic and real point cloud datasets are tested for the use of the robust algorithm.

Commentary by Dr. Valentin Fuster
2017;():V001T02A010. doi:10.1115/DETC2017-67630.

Operation sequencing is one of crucial tasks for process planning in any CAPP system. In this study, a novel discrete particle swarm optimization (DPSO) approach is proposed to solve the operation sequencing problems in CAPP. To find the process plan with lowest machining cost efficiently, the DPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints among operations. In the DPSO, a FOS is directly represented by a permutation via a particle and the fragment crossover based updating mechanism is developed to evolve the particles. Furthermore, the fragment mutation for altering FOS and the uniform mutation for changing machine, cutting tool and tool access direction for each operation are incorporated into the DPSO to improve exploration ability. A case study involving two prismatic parts are used to verify the performance and efficiency of the DPSO. The comparison between the DPSO and two existing PSOs as well as an existing genetic algorithm shows promising higher performance of the DPSO with respect to solution quality for operation sequencing.

Commentary by Dr. Valentin Fuster
2017;():V001T02A011. doi:10.1115/DETC2017-68121.

The following paper aims to create a classification method for design enablers which will later be used to measure the impact of design enablers on industry. First, a classification method for design enablers is developed based on a review of relevant literature. Second, this method is applied to a series of design enablers developed by the CEDAR lab strictly based on information published within literature. Finally, non-published documentation available for one of the reviewed design enablers will be considered further to better illustrate the classification method. In later work, additional unpublished documentation will be considered alongside interviews of design enabler developers to fully classify the remaining enablers. This research will later be used to map design enabler development to its impact in order to make recommendations for decision making for future design enabler development.

Topics: Design
Commentary by Dr. Valentin Fuster
2017;():V001T02A012. doi:10.1115/DETC2017-68269.

Hybrid assembly cells allow humans and robots to collaborate on assembly tasks. We consider a model of the hybrid cell in which a human and a robot asynchronously collaborate to assemble a product. The human retrieves parts from a bin and places them in the robot’s workspace, while the robot picks up the placed parts and assembles them into the product. Realizing hybrid cells requires -automated plan generation, system state monitoring, and contingency handling. In this paper we describe system state monitoring and present a characterization of the part matching algorithm. Finally, we report results from human-robot collaboration experiments using a KUKA robot and a 3D-printed mockup of a simplified jet-engine assembly to illustrate our approach.

Commentary by Dr. Valentin Fuster
2017;():V001T02A013. doi:10.1115/DETC2017-68390.

Additive manufacturing (AM) has enabled control over heterogeneous materials in ways that were not previously possible. This paper presents a novel method for representing and communicating heterogeneous materials based structures that include tolerancing of geometry and material together. AM has expanded design possibilities to include specified material heterogeneities, including functionally graded materials. The aim of the paper is to propose a means to specify nominal materials and allowable material variations in parts, including (a) explicit material transitions and (b) functional transitions to support single and multiple material behaviors. The transition region combines bounded regions (volumes and surfaces) and material distribution equations. Tolerancing is defined at two levels, that of the geometry including bounded regions and that of the materials. Material tolerances are defined as allowable material variations from nominal material fractions within a unit volume at a given location computed using material distribution equations. The method is described thorough several case studies of abrupt transitions, lattice based transitions, and multi-material transitions.

Commentary by Dr. Valentin Fuster
2017;():V001T02A014. doi:10.1115/DETC2017-68391.

The purpose of this paper is to develop Tolerance-Maps (T-Maps) for composite position tolerance applied to patterns (arrays) of features. The T-Map (Patent No. 6963824), is a range of points obtained by mapping all the variational possibilities of a feature within its tolerance-zone to a hypothetical Euclidean point space. T-Maps have already been developed for tolerances applied to features, such as a simple axis (line), a plane, a cylinder, etc., but not for patterns of features. In this paper, the developed T-Map model will be shown to be sensitive to the effects of composite position tolerance, two-single segment control frames and material modifiers. Two levels of T-Maps are proposed for a pattern of features: assembly level (ensuring assembly of engaging pattern of features) and part level (map of variations of entire pattern as a whole). The use of assembly level and part level T-Map is demonstrated with a pattern of features. The pattern of features considered is two-dimensional patterns of pins and holes that are intended to engage (e.g. an integrated circuit and its plug-in base).

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Design Informatics

2017;():V001T02A015. doi:10.1115/DETC2017-67082.

Authors belonging to different institutions (‘schools’) of cyber-physical systems (CPSs) research and development report on largely different objectives, underpin their work with different theories and methodologies, and target characteristics which can actually better characterize other categories and families of engineered systems. This has resulted in an ontological chaos. Therefore, our research addressed the question: What exists in the form of past, current and future CPSs? Our hypothesis has been that we can have an ordered picture on the landscape of CPSs by introducing the notion of system generation. Generation is a structural term defined as a ‘technological/engineering cohort’ of different individual manifestation of systems that reflect genotypic features of ancestor systems belonging to the same category, but deviates from them with regards to their phenotypic features. Based on our literature findings, we have defined five generations of CPSs, which could be differentiated based on: (i) the level of self-intelligence, and (ii) the level of self-organization. The zeroth generation includes look-alikes and partial implementations of CPS. The 1G-CPSs include systems with self-regulation and self-tuning capabilities, while the 2G-CPSs are capable to operationalize self-awareness and self-adaptation. The 3G-CPSs are equipped with the capabilities of self-cognizance and self-evolution. According to our reasoning model, only the fourth generation of CPSs is supposed to achieve self-consciousness and self-reproduction in the form of system of systems. The paper analyses the major paradigmatic characteristics of these generations. It also provides an outlook to the trends that may have strong influence on the introduced generations of CPSs.

Topics: Chaos
Commentary by Dr. Valentin Fuster
2017;():V001T02A016. doi:10.1115/DETC2017-67427.

The objective of this research is to investigate the viability of a decision support system for technical instruction authors who write instructions in free text. The foundation for the decision support system relies on mapping computational linguistic metrics to guidelines for authoring technical instructions. For example, the guideline Limit each sentence to 25 words or fewer maps to the computational linguistic metrics Word Count. As another example, the guideline Begin each step with a command (an imperative verb) maps to the Location of first imperative verb metric. Testing the decision support system shows its effectiveness and suggests a need to expand the computational rule-base to include even more guidelines. Doing so can further enhance the usability of the decision support system in writing environments. Faculty and students in academia and employees in industry need such a system to improve the quality of written instructions, accelerate revisions, and enhance productivity. In summary, a rule-base for providing feedback to technical authors has been investigated and established. With this rule-base as a foundation, a decision support system has been developed and tested, and the source code has been made publically available.

Topics: Testing , Feedback , Students
Commentary by Dr. Valentin Fuster
2017;():V001T02A017. doi:10.1115/DETC2017-67493.

Model-based product design using computer simulation has become a standard design practice in most companies in mechanical engineering. However, there is a need for efficient simulation tools that can provide design-supporting information already at early design phase when the most important decisions are made. Design process and design tools need to be agile and enable iterative process where the design and its requirements can effectively be iterated.

Low-fidelity models can be part of the solution for time issue in early design phase. Low-fidelity prototypes are simplified representations of functions and concepts in the virtual prototype. Axiomatic design with low-fidelity modelling approach is a promising concept for achieving design-supporting information in an efficient way. In this method, there is a linear mapping between design parameters and system characteristics. Non-linear models of the system are linearized at the nominal point. An engineering design analysis tool (EDA tool) to enhance EDA is constructed and presented in this paper.

For evaluation of the usefulness of this tool, a case study is presented. The case study deals with a simple hydraulic crane that is manufactured from steel plate. The results of the case study design are compared with results achieved with conventional CAD and FEM tools. Modelling accuracy and required modelling and simulation efforts are compared in both cases.

Commentary by Dr. Valentin Fuster
2017;():V001T02A018. doi:10.1115/DETC2017-67910.

Maintenance costs are a main cost driver for offshore wind energy. Prediction of failure and particularly failure understanding can help to bring these costs down significantly. Since the wind turbine is subjected to a large number of dynamic events it is important to fully understand the turbine response to these events. Pattern mining has been used successfully for different applications. We believe it to have large potential for understanding turbine behavior based on turbine status logs. These logs record all turbine actions and can be used as input for pattern mining algorithms. This paper proposes the use of a multi-level pattern mining approach in order to minimize the number of uninteresting patterns and facilitate response understanding. The paper mainly focuses on the extraction of patterns and association rules linked to certain alarms and how they can be annotated for further use in the multi-level pattern mining approach. Several years of wind turbine data is used. The use of the approach is illustrated by detecting the characteristic pattern linked to turbine response to an Extremely High Wind Speed Alert.

Commentary by Dr. Valentin Fuster
2017;():V001T02A019. doi:10.1115/DETC2017-68352.

Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1,000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation processes.

Topics: Design
Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Design, Simulation and Optimization for Additive Manufacturing

2017;():V001T02A020. doi:10.1115/DETC2017-67282.

Additive manufacturing (AM) exemplifies the potential of lattice structures to revolutionize structural design. It enables light weight lattice structures to be produced while maintaining the desirable structural performance. Lattice design can vary in different shapes and dimensions. Obtaining the structural performance of a particular lattice structure design is not a straight-forward process. Significant effort is required to perform mechanical testing experiments or to perform finite element analysis (FEA) to characterize the lattice design. In view of that, a guidance system to determine lattice design parameters based on desired functional performance for a specific lattice type is developed, which can be used in interactive design processes and workflows. Homogenization using FEA experiments is applied to characterize the macroscopic lattice structural properties. Mechanical properties of orthotropic cubic lattice f2ccz are estimated. It follows with a design of experiment study to characterize the effective structural properties of 39 lattices with respect to lattice design parameters (unit cell length and strut diameter). A Gaussian process is applied to develop models relating the lattice design parameter to macroscopic structural properties (forward model), and vice versa (inverse model). Both the forward and inverse models are examined and shown to be capable of modeling the FEA experimental dataset of 39 lattices. To illustrate the potential application of the lattice design advisor framework, a structural design use case including lattice part is presented. In the use case, the lattice structure design advisor is proven to be able to estimate an accurate homogenized material property of arbitrary lattice design parameter. This lattice structure design advisor can simplify and streamline the design, modeling and simulation process of lattice-filled structural designs.

Commentary by Dr. Valentin Fuster
2017;():V001T02A021. doi:10.1115/DETC2017-67538.

Fused Deposition Modeling (FDM - a technology of additive manufacturing) parts entail a certain amount of ambiguity in terms of its material properties and microstructure due to its manufacturing technique. Therefore, an FDM part differs from its design model in terms of strength and stiffness. With an increasing amount of FDM parts being used as end use products, it is necessary to simulate and analyze them. Due to the differences in microstructure and material properties of FDM parts, it is necessary to determine the accuracy of analysis methods like Finite Element Analysis (FEA) while analyzing the non-continuous, non-linear FDM parts. The goal of this study is to compare FEA simulations of the as-built geometries with the experimental tests of actual FDM parts. A dogbone geometry with different infill patterns is tested under tensile loading. Further, as-built 3D models are simulated using FEA and the stress results are compared with experimental data. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors.

Commentary by Dr. Valentin Fuster
2017;():V001T02A022. doi:10.1115/DETC2017-67572.

One of the most important steps in the preprocessing stage of fused deposition modeling is the generation of a set of instructions that control every movement of the tool head, known as GCode, which is created by a slicer software. The tool path is dependent on various user defined inputs including infill pattern, infill density, layer height, and feed rate. All current slicers generate the path explicitly and do not consider certain geometries that will create critical voids such as thin walls and small corners. This work replicates a new implicit slicing method in which functionally derived infill patterns are overlaid onto each layer of a part reducing the possibility of voids and flaws. Further research investigating the effects of varying implicit infill patterns have on mechanical properties is also included. Stress and strain data is gathered for three different test cases, and the resulting mechanical properties for each case are compared.

Commentary by Dr. Valentin Fuster
2017;():V001T02A023. doi:10.1115/DETC2017-67591.

The microstructural characteristics of materials processed via powder-based additive manufacturing (PAM) methods can be significantly different from those made by conventional manufacturing process using the same material. In an effort to link PAM process parameters with the functional performance of manufactured part, it is necessary to identify the effect of the special microstructural features generated by PAM on the final constitutive response of the relevant materials. In the present study, a microstructure-informed constitutive model is developed to describe the mechanical behavior of solidified material produced by PAM processes. The model is based on crystal plasticity and accounts for the effect of grain size and aspect ratio of the microstructure. The effect of these dominant features is captured by considering a core and mantle configuration for the grain volume, and by introducing a grain boundary influence region. The constitutive model’s ability to capture the grain size and shape effect is demonstrated by simulating the stress-strain behavior under uniaxial loading of a representative volume element (RVE) with columnar microstructure characterized by a range of grain sizes and aspect ratios.

Commentary by Dr. Valentin Fuster
2017;():V001T02A024. doi:10.1115/DETC2017-67596.

Additive Manufacturing (AM) encompasses a set of fabrication technologies that are being used with increasing frequency in a wide variety of scientific and industrial pursuits. These technologies, which operate by successive additions of material to a domain, enable the manufacture of highly complex geometries that would otherwise be unrealizable. However, the material micro and meso-structures generated by AM processes differ remarkably from those that arise from conventional techniques and occasionally introduce unwanted functional features; this has been an obstacle to the use of AM in some applications. In the present work, we propose a multiscale method that utilizes the unique meso-scale structuring capabilities of implicit slicers for AM, in conjunction with existing topology optimization tools for the macro-scale, in order to generate functional components. The use of this method is demonstrated on the example of a hand tool. We discuss the applications of this methodology, its current limitations, and the future work required to enable its widespread use.

Commentary by Dr. Valentin Fuster
2017;():V001T02A025. doi:10.1115/DETC2017-67597.

Recent years have seen a sharp increase in the development and usage of Additive Manufacturing (AM) technologies for a broad range of scientific and industrial purposes. The drastic microstructural differences between materials produced via AM and conventional methods has motivated the development of computational tools that model and simulate AM processes in order to facilitate their control for the purpose of optimizing the desired outcomes. This paper discusses recent advances in the continuing development of the Multiphysics Discrete Element Method (MDEM) for the simulation of AM processes. This particle-based method elegantly encapsulates the relevant physics of powder-based AM processes. In particular, the enrichment of the underlying constitutive behaviors to include thermoplasticity is discussed, as are methodologies for modeling the melting and re-solidification of the feedstock materials. Algorithmic improvements that increase computational performance are also discussed. The MDEM is demonstrated to enable the simulation of the additive manufacture of macro-scale components. Concluding remarks are given on the tasks required for the future development of the MDEM, and the topic of experimental validation is also discussed.

Commentary by Dr. Valentin Fuster
2017;():V001T02A026. doi:10.1115/DETC2017-67600.

The freedom of design that is afforded by Additive Manufacturing (AM) processes opens exciting possibilities for the production of lightweight, high performance components and structures. Consequently, in recent years the development of software tools to enable engineering design methods that exploit the unique features of AM has become a subject of increased research interest. In this paper we explore the use of Topology Optimization (TO) algorithms to tailor component shape in order to achieve the intended functionality of additively manufactured components at the macro length scale. We present two case studies: the first concerns the hierarchical nesting of functions in a hand tool, while the second covers the development of a metamaterial component substructure for an Uninhabited Underwater Vehicle (UUV) hull. We offer conclusions regarding the usefulness of TO techniques for the design of AM components, and a summary of future work, which we feel is necessary to improve such methodologies.

Commentary by Dr. Valentin Fuster
2017;():V001T02A027. doi:10.1115/DETC2017-67633.

Selective laser melting (SLM) is a powder bed based additive manufacturing process by melting fine-grained metallic powders with a laser heating source. Understanding the solidification of alloys during SLM process is of importance for accurate prediction of microstructures and properties for process design and optimization. In this study, a multi-physics model is developed to simulate evolution of alloy microstructure during solidification, which incorporates heat transfer, fluid dynamics, kinetics of phase transformations, and grain growth. In this integrated simulation framework, the phase field method for the dendritic growth of a dilute binary alloy is coupled with the thermal lattice Boltzmann method for the melt flow and heat transfer. The effects of latent heat, melt flow and cooling rate on solidification process are also investigated. The multi-physics simulation results provide new insight to predict the complex solidification process more accurately than single-physics approaches.

Commentary by Dr. Valentin Fuster
2017;():V001T02A028. doi:10.1115/DETC2017-67807.

Recent studies have shown advantages to utilizing metamodeling techniques to mimic, analyze, and optimize system input-output relationships in Additive Manufacturing (AM). This paper addresses a key challenge in applying such metamodeling methods, namely the selection of the most appropriate metamodel. This challenge is addressed with domain-specific AM information, derived from physics, heuristics and prior knowledge of the process. Domain-specific input/output models and their interrelationships are studied as a basis for a domain-driven metamodeling approach in AM. A metamodel selection process is introduced that evaluates global and local modeling performances, with different AM datasets, for three types of surrogate metamodels (polynomial regression (PR), Kriging, and artificial neural network (ANN)). A salient feature of this approach is its ability to seamlessly integrate domain-specific information in the model selection process. The approach is demonstrated with the aid of a metal powder bed fusion (PBF) case study and the results are discussed.

Commentary by Dr. Valentin Fuster
2017;():V001T02A029. doi:10.1115/DETC2017-67888.

Despite increasing levels of acceptance, traditional additive manufacturing techniques continue to suffer from a number of fundamental drawbacks that act to limit broad adoption. These drawbacks include limits on processable materials, part properties/performance, geometric deviation and repeatability. The vast majority of existing processes also rely on a point-by-point approach to generate parts, resulting in exceedingly long build times and extremely poor scaling behavior. Furthermore, in general, current systems require significant levels of complexity for operation, resulting in the need for considerable upfront capital investment as well as continuing maintenance costs. A new manufacturing approach is presented here, based upon the generation of objects from the direct creation of constituent volumetric sub-regions. This process addresses many of the limitations described above, and has the potential to significantly alter the manner with which three-dimensional objects are realized.

Commentary by Dr. Valentin Fuster
2017;():V001T02A030. doi:10.1115/DETC2017-68149.

Powder-based additive manufacturing technologies introduce severe variations in microstructure in terms of grain size and aspect ratio that, coupled with porosity, can result in dramatic effects on the functional (mechanical, thermal, fatigue, fracture etc.) performance of as-produced parts. In the context of Integrated Computational Materials Engineering (ICME), it is essential develop a computationally efficient approach for generating synthetic microstructural morphologies that reflect these process-induced features. In the present paper, we employ two methodologies for computing the evolution of metal solidification at the microstructural level as a function of process parameters associated with additive manufacturing. The first method is the Continuum Diffuse Interface Model (CDM) applied to an arbitrary material system, and the second, the Multi-Phase Field Model (MPFM) applied to pure nickel (Ni). We present examples of microstructures generated by these methods within the context of additive manufacturing.

Commentary by Dr. Valentin Fuster
2017;():V001T02A031. doi:10.1115/DETC2017-68157.

Additive Manufacturing (AM)’s advance from rapid prototyping to the end-of-use products inevitably challenges conventional design theories and methodologies. Especially while adopting systematic engineering design methodologies to design for AM (DfAM), it is essential to develop new design methods that explore the new design space enabled by AM’s design freedom from the early design stage. To address the challenge, this study provides a new design framework and a design method for modeling AM-enabled product behaviors in the conceptual design phase of DfAM. Firstly, this study contrasts function-based methods with affordance-based methods. The device-centric, form independent and input/output-based transformative properties of the function-based methods such as function decompositions have strengths in modeling product’s internal behaviors. However, the function-based methods show limitations in the new area of AM-enabled mass personalization which requires design approaches for representing user-centric structural design requirements acquired only by AM’s design freedom. On the other hand, the affordance-based methods can address the function-based methods in DfAM due to their user-centric (artifact-user interactive), form dependent and non-transformative properties. After the contradiction, we propose an affordance-based DfAM framework and an affordance structure as a formal modeling technique for AM-enabled personalized product behaviors. A case study of a trans-tibial prosthesis socket provides an illustration in this study. The contribution of the study is in developing a design method for the conceptual design phase of DfAM that fulfills the objectives of achieving AM-enabled mass personalization with systematic engineering design approaches.

Commentary by Dr. Valentin Fuster
2017;():V001T02A032. doi:10.1115/DETC2017-68289.

Rapid development in the field of additive manufacturing, evidenced, in part, by the proliferation of low cost 3D printing, has accelerated the prototyping and design evaluation stages of the product development cycle. 3D printed structures have shown variations in their material properties as a function of the printing orientation. Moreover, thermoplastic materials which are often used as filament materials for 3D printing are known to have dependency on temperature, frequency and strain rate. Hence, the aim of this research is to estimate the variations in the complex modulus of the printed materials as a function of printing direction. This will allow an estimation of the variation in the vibration characteristics (natural frequencies, damping) of the printed structures as a function of printing direction. To this end, PLA beams were printed in four different orientations. A dynamic mechanical analyzer was used to measure mechanical properties of the printed beams. By using a curve fit method, the frequency and temperature dependent complex modulus is estimated. These complex moduli are used for estimating the eigenvalues of a non-dimensional beam. The observed variability in the vibration behavior as a function of the printing orientation is summarized here.

Commentary by Dr. Valentin Fuster
2017;():V001T02A033. doi:10.1115/DETC2017-68293.

Like many other additive manufacturing processes, FDM process is driven by a moving heat source, and temperature history plays an important role in determining the mechanical properties and geometry of the final parts. Thermal simulation of FDM is challenging due to geometric complexity of manufacturing process and inherent computational complexity which requires numerical solution at every time increment of the process.

we describe a new approach to thermal simulation of the FDM process, formulated as an explicit finite difference method that is applied directly on as-manufactured model described by a typical manufacturing process plan. The thermal model accounts for most relevant thermal effects including heat convection and radiation to the environment, heat conduction with build platform and between adjacent roads (and adjacent layers). We show that the proposed simulation method achieves linear time complexity. This implies that the simulation not only scales to handle 3D printed components of arbitrary complexity but also can achieve real-time performance.

Topics: Simulation
Commentary by Dr. Valentin Fuster
2017;():V001T02A034. doi:10.1115/DETC2017-68330.

Design for Additive Manufacturing is an evolving field that allows alternative design approaches to facilitate improvements in parts and builds by taking advantage of the capability of additive manufacturing (AM). Currently, available CAD software does not provide sufficient tools for AM designers, which results in a complex iterative process requiring multiple file types and programs. The complicated process of generating build time and cost estimates prevents designers from being able to efficiently optimize their parts for the AM process. Through the Solidworks Application Programming Interface a user-controlled macro was developed to generate build time and cost estimates by automatically creating support structures from multiple planes of comparative Ray Trace vector grids. The macro provides the user with visual and qualitative part information at the first stage in the design/file workflow, curtailing the current complex workflow to reduce overall design time. The macro is focused on the material extrusion process due to the diversity in available machines and build control, favoring user knowledge of specific parameters to calculate the build time and cost. Limitations of the approach along with extensions to other AM processes are also discussed.

Commentary by Dr. Valentin Fuster
2017;():V001T02A035. doi:10.1115/DETC2017-68446.

Additive manufacturing (AM) is gaining popularity in industrial applications including new product development, functional parts, and tooling. However, due to the differences in AM technologies, processes, and process implementations, functional and geometrical characteristics of manufactured parts can vary dramatically. Planning, especially selecting the appropriate AM process and material requirements can be rather involved. Manufacturability using AM processes has been well studied; however, gaps exist in the design process when catering to the needs of manufacturability. Designers today are challenged with a lack of understanding of AM capabilities, process-related constraints, and their effects on the final product. Challenges are compounded by the ambiguity of where design for AM ends and process planning begins. These ambiguities can be addressed through design principles and corresponding design rules for additively manufacturing parts. The purpose of this paper is to categorically present relevant and reported efforts in design and process planning with design rules in AM. The overarching goal of the review is to offer insights to extract and categorize fundamental principles for derivative rules for different AM processes. Identifying such fundamental requirements could potentially lead to breakthroughs in design and process planning.

Commentary by Dr. Valentin Fuster
2017;():V001T02A036. doi:10.1115/DETC2017-68457.

As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these production hurdles requires a further understanding of geometry-process-structure-property relationships for additively manufactured parts. In smaller sample sizes, empirical approaches that seek to harness data have proven to be effective in identifying material process-structure-property relationships. This paper presents a collaborative AM data management system developed at the National Institute of Standards and Technology (NIST). This data management system is built with NoSQL (Not Only Structured Query Language) database technology and provides a Representational State Transfer (REST) interface for application integration. In addition, a web interface is provided for data curating, exploring, and downloading. An AM data schema is provided by NIST for an alpha release, as well as a set of data generated from an interlaboratory study of additively manufactured nickel alloy (IN625) parts. For data exploration, the data management system provides a mechanism for customized web graphic user interfaces configurable through a visualization ontology. As a collaboration platform, the data management system is set to evolve through sharing of both the AM schema and AM development data among the stakeholders in the AM community. As data sets continue to accumulate, it becomes possible to establish new correlations between processes, materials, and parts. The functionality of the data management system is demonstrated through the curation and querying of the curated AM datasets.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Emotional Engineering

2017;():V001T02A037. doi:10.1115/DETC2017-67050.

Improvement in labor productivity is a common problem in each country. In particular, in Japan where the productive age population is decreasing, it is necessary to steadily advance efforts for the improvement at various sites throughout the entire society. In doing so, in order to make it prevail over broad areas, it is necessary to consider that on-site holding resources can be utilized, that it can be easily introduced, and that it will surely be effective. Therefore, we have focused on improving the way of collaboration from an angle of “person’s personality”. Specifically, we have aimed to build a methodology, for a “team” that is a collaborative form widely introduced and utilized at companies and educational sites, which enhances the effectiveness of team activities. As for the overall structure of the methodology, we have designed it with a three-layer structure in order to clarify separation from other intellectual properties and consideration to ethical aspects. That is, the designed methodology comprises the following three phases. Phase I: a team formation methodology; Phase II: a team management methodology; and Phase III: a team development support methodology.

Study results of Phase I were presented at the 26th Design Engineering and System Division Lecture by the Japan Society of Mechanical Engineers (JSME). In this study, we have used the methodology of Phase I to devise, as a method included in Phase II, three Rules for creating a team environment that makes it easy for “introverted” persons in Jungian psychology to express their opinions. Then, we have conducted parallel group randomized trials comparing an intervention group with a control group, analyzed the results by an analysis method such as Data Envelopment Analysis (DEA), and verified the effectiveness of the devised method. As a result, the findings have revealed that teams in which the team management was carried out according to the devised method tends to be more effective and prone to excellent effectiveness.

Topics: Teams
Commentary by Dr. Valentin Fuster
2017;():V001T02A038. doi:10.1115/DETC2017-67244.

Dyeing involves fixing a dye on a cloth, or creating a dyeing pattern. Because dyeing patterns depend on the physical properties of fibers, dyes, and dyeing technique that are employed, predicting a finished dyeing pattern is difficult even for artisans. Because dyeing is an irreversible phenomenon that requires a lot of time, to accurately predict a completed pattern would improve the efficiency of dyeing and reduce its costs.

In this paper, we propose a digital method for designing dyeing patterns based on a simulation of the dyeing process. Dyeing experiments was conducted to model dyeing process accurately. From experiments, we defined the dyeing process is combination of two phenomena: capillary phenomenon and diffusion phenomenon. In the proposed method, integrated these two phenomena by using the cellular automaton method and generate dyeing patterns that produce different results depending on the pattern-generating parameters.

The thickness of yarn and spaces between yarns in fabric is not uniform because of the influence of spinning and weaving. Therefore, in the proposed method, we use the fluctuation property, which is inherent in nature, to generate a dyeing pattern that preserves a natural impression.

Based on the simulation of the proposed dyeing process, we developed a system that generates patterns based on KANSEI. Associating KANSEI with pattern generation parameters produces dyeing patterns that exhibit the required impressions (KANSEI) for the generated dyeing patterns. Based on this development method, we constructed a basic system for pattern generation and verified the effectiveness of the method.

Commentary by Dr. Valentin Fuster
2017;():V001T02A039. doi:10.1115/DETC2017-67340.

Personalized and timely feedback has the potential to improve an individual’s performance on a wide variety of engineering tasks. The ability to capture an individual’s affective state(s) and performance on a task is a key component needed to advance personalization of feedback. While automated methods exist for quantifying task performance, the ability to quantify an individual’s affective state(s) remains an open research area. Existing methods for quantifying an individual’s affective state(s) are challenging to implement where real-time assessment is needed (e.g., engineering workshop environments). This has sparked a growing interest for automated systems capable of inferring individuals’ affective state(s), based on their projected facial or body cues. However, existing methods attempt to employ a general model to label an individual’s affective state(s) into discrete categories, such as fear, joy, surprise, etc. Nonetheless, emotional expressions are far more complex, as individual differences in facial expressions, may deteriorate the performance of these systems in providing personalized feedback. To overcome these limitations, this work proposes a machine learning method for predicting an individual’s performance on a task by utilizing his/her unique facial keypoint data, hereby bypassing the need to infer his/her discrete affective states. A case study involving 31 participants is presented. The support vector machine model employed to predict an individual’s performance yielded an accuracy of 77.15% for an individual-task specific model. In contrast, a general model yielded an accuracy of only 52.69%, hereby supporting the authors’ argument that individual-task specific models are more suitable for advancing personalized feedback.

Topics: Mining , Feedback
Commentary by Dr. Valentin Fuster
2017;():V001T02A040. doi:10.1115/DETC2017-67435.

Motion Control is increasing its importance. Although the progress of system dynamics is remarkable, progress of human body motion control is very slow. Most of system dynamics deal with explicit knowledge, but human body motion control belongs to tacit knowledge.

Its difficulty is the number of degrees of freedom is tremendously large and human behaviors change very flexibly to cope with the changing contexts of environments and situations. Further, our body motions vary from person to person, because our bodies, muscles and joints are different. These problems make it very difficult to deal with human body motions.

Although there are many researches using motion capture, EMG, etc., they succeeded only in showing how final successful movements should be. They can show movements at each step toward this goal, but they cannot teach learners how they should coordinate their muscles or joints. Coordination or balancing plays an important role in body motion learning, But, there are very few, in any, researches which help learners learn how to coordinate or balance their muscles and joints to achieve the final successful movement.

In this paper, a solution to how we can help a learner learn to coordinate or balance in motion or motor learning is introduced. Its approach is pattern based and it uses Recognition Taguchi (RT) technique, one of the techniques of Mahalanobis Taguchi Systems. In this approach, Mahalanobis Distance (MD) is used to indicate quantitatively how a learner’s pattern of movement is close to the successful one. MD reduces multi-dimensional information to one-dimensional. RT indicates how a sample pattern matches the ideal pattern quantitatively using MD. In the regular RT approach, Unit Space (Ideal Pattern) is defined and each sample space is compared with Unit Space using MD.

But In this work, Unit Space is updated every time a learner succeeds, such as successfully riding a bicycle. And every trial movement is compared with this updated Unit Space. The primary benefits of RT are it can process large data in a very short time and it is based upon the difference between the ideal pattern and the current pattern. So, learners can understand which joints they should pay attention to in order to coordinate or balance to improve their movements. Thus, step by step, they can coordinate or balance their muscles and joints to get closer to the ideal movement.

Topics: Motion control
Commentary by Dr. Valentin Fuster
2017;():V001T02A041. doi:10.1115/DETC2017-67555.

In current CAD software the process of assembly modeling is hindered by a large number of separate rotation and translation actions necessary, especially in case of larger assemblies. Additionally matching faces, edges or points must be selected by clicking to define the appropriate constraint. In contrast to that, the process of assembling two normal sized physical parts in the real world seems to be rather simple. That is because we know how to grasp and move objects with our hands intuitively from our everyday experience. The idea behind this contribution is to enable the product developer to assemble CAD parts in a virtual environment through natural finger interaction like in reality. Therefore we present an overall method that combines the natural finger interaction with virtual objects and the insertion of constraints between rotationally symmetric CAD parts. The developed algorithms identify matching surfaces on the basis of the geometry as well as position and orientation of the parts in 3D space. This paper highlights the method to use a combination of real-time physics simulation and a heuristic approach to achieve an intuitive interaction interface. Additionally, we describe the detection algorithms developed to find assembly relationships between rotationally symmetric CAD parts without prior constraint definition. We also present a prototype system to demonstrate the functionality of the overall method. Furthermore, challenges for future research, such as extending the functionality of the detection algorithms on additional part types, like non-rotationally symmetric shapes, are discussed.

Commentary by Dr. Valentin Fuster
2017;():V001T02A042. doi:10.1115/DETC2017-68089.

Customers with diversified tastes have come to expect more than high product quality and performance. A high level of added value is required, and the provision for products that meet the individual requirements customers has become important. In recent years, delight design has attracted attention as a method for enhancing customer satisfaction. Delight design describes a design that offers attractive qualities beyond the required performance and quality requirements. However, attractiveness depends on the quality of the designer; furthermore, there are difficulties associated with the definition and expression based on similarities between the actual design and the original idea.

Therefore, in this study, we have proposed two methods for constructing neural network models: one for a value model using customer KANSEI data and one for an evaluation model for customers (designed as an inverse model). The customer value model creates the required product design from KANSEI. The evaluation model analyzes KANSEI for the individual customer and creates a design (delight design) according to KANSEI engineering and the preferences of the individual customer. The method proposed in this study was applied to the crack pattern seen on ceramic surfaces.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Human Modeling-Methods and Applications in Engineering

2017;():V001T02A043. doi:10.1115/DETC2017-67224.

Most occupant accommodation assessments of a new vehicle design currently still utilize human appraisal. That is, human subjects experience the new design physically and provide feedback including a numerical rating or verbatim description. There are two drawbacks with this type of assessment: 1) the outcome is subjective. They are likely affected by other factors such as the vehicle’s appearance or brand and the individual’s own bias; 2) the outcome may not be able to reveal where the issues are nor how to resolve them.

The digital manikin technology has been widely used in different areas: starting from movie and video gaming industries, and getting more and more involved in the product life circle of manufacture industries. Human motions are captured, and the digital manikin is utilized to present these motions virtually. This paper introduces a method that uses digital manikins to assist the process of vehicle design. Subjects’ motions interacting with a vehicle, which are related to a new design change, are captured. These motions are used to drive digital manikins that represent their respective subjects in size and body shape. A software system that animates the digital manikin according to the motions and creates swept volumes of selected body segments was created. The collection of the swept volumes of all subjects represents the space that is occupied by the human body during the motion. This space can be used to assess the design changes by indicating the minimum clearance between the swept volume and vehicle components or the interference between the human body with the components. In addition, the space described by the swept volumes provides a guideline or space limit for any future design changes.

This method is objective. It not only pin-points the locations that cause discomfort or inconvenience by the new design, but also provides quantitative suggestions on how much improvement is needed for a better design.

Commentary by Dr. Valentin Fuster
2017;():V001T02A044. doi:10.1115/DETC2017-67452.

With the advances in hardware and process development, additive manufacturing is realizing a new paradigm: mass customization. There are massive human-related data in mass customization, but there are also many similarities in mass-customized products. Therefore, reusing information can facilitate mass customization and create unprecedented opportunities in advancing the theory, method, and practice of design for mass-customized products. To enable information reuse, different models have to be aligned so that their similarity can be identified. This alignment is commonly known as the global registration that finds an optimal rigid transformation to align two three-dimensional shapes (scene and model) without any assumptions on their initial positions. The Super 4-Points Congruent Sets (S4PCS) is a popular algorithm used for this shape registration. While S4PCS performs the registration using a set of 4 coplanar points, we find that incorporating the volumetric information of the models can improve the robustness and the efficiency of the algorithm, which are particularly important for mass customization. In this paper, we propose a novel algorithm, Volumetric 4PCS (V4PCS), to extend the 4 coplanar points to non-coplanar ones for global registration, and theoretically demonstrate the computational complexity is significantly reduced. Several typical human-centered applications such as tooth aligner and hearing aid are investigated and compared with S4PCS. The experimental results show that the proposed V4PCS can achieve a maximum of 20 times speedup and can successfully compute the valid transformation with very limited number of sample points.

Topics: Algorithms
Commentary by Dr. Valentin Fuster
2017;():V001T02A045. doi:10.1115/DETC2017-67456.

Stroke basically consists in brain-cells death due to lack or excess of blood. Stroke has many important consequences and falls are one of the most concerning. Falls can produce several injures from minor lacerations to fractures and death. It has been found that balance and gait impairments after stroke are important risk factors for fall. Hence, improving balance and gait ability in stroke survivors can significantly reduce falls rate. In this literature review, we review the main characteristic and the therapeutic results of different therapeutic interventions aimed at improving balance and walking ability. The main therapeutic interventions included are the Bobath therapy, exercise-based interventions, orthotic and assistive devices, modality treatments, alternative therapies, robotic-assisted training, and computational-based interventions. The parameters considered as evidence of balance and/or gait recovery after a specific intervention are: walking speed (WS), cadence, endurance, stride/step length, weight/walking symmetry, and sway. Our main findings are: 1) The wide use of the Bobath concept is not well supported by evidence due to its performance has been found to be inferior to some exercises-based interventions such as walking training; 2) exercises-based interventions were classified as strength and task-specific training. The former improves muscular and bone health, aerobic capability, and prepares the patient to perform a more demanding activity. The latter is designed as a repetitive training of a functional activity, mainly walking, and sit to stand exercises, which improve both gait and balance. Orthotic and assistive devices have effects on balance and gait but only while they are worn or used; 3) robotic assisted walking-training presented similar results to overground or treadmill walking training in terms of walking speed and balance recovery. However, the most important advantage lies on the reduction of burden for therapists; 4) thee most important use of motion analysis is as a tool for identify the causes deficits in a patient and the to design a therapy in accordance; 5) motion synthesis can be used as a tool to answer very specific questions related to capabilities/limitations of a patient. For instance, “what would be the effect of increasing hip-torque capability of a stroke survivor on the walking-symmetry?” The answer to this question would either help to design an exercise/intervention or to discard such intervention due to low impact; 6) some treatments are added to a main therapy to increase its effect on a given parameter. Functional electrical stimulation, which is added to cycling training to improve motion patterns. Biofeedback is used during balance training to reduce weight-asymmetry. And virtual reality and video games are used to increase motivation and permanence of patient on a therapy; 7) we found some alternative or no widely used therapies. Among the most promising we can mention Tai-Chi exercises, which integrates physical and mental activities to improve balance and gait and rhythmic auditory stimulation that improves WS and weight-symmetry; and 8) orthotics devices help to reduce falls by extending the base of support but the effect appears only while they are worn. In general, there is not an ultimate therapy able to fit to every patient. The choice should depend on patient’s goals and conditions. Moreover, falls can not be eliminated but they can be substantially reduced by improving balance and gait.

Commentary by Dr. Valentin Fuster
2017;():V001T02A046. doi:10.1115/DETC2017-67783.

The fusion surgery is a standard treatment for scoliosis. Fatigue-related failure is one common cause for the fusion surgery implant. Due to the high cost of revision surgery, it is of clinical value to study the fatigue behaviors of the spinal implants under physiological spinal loads. In the literature, biomechanical tests and finite element (FE) methods have been used to study the fatigue of the spinal implants. Compared with biomechanical tests, FE analysis has the advantage of low cost and high efficiency. Due to the high computational cost, no FE study has been modeled the exact geometry of the pedicle screw (including the thread) in the screw-bone connection within the multi-level spine FE model. This study introduced a feasible FE-based method to predict the fatigue behaviors of the spinal implants with exact geometry of pedicle screw. One previously-validated FE spine model was utilized to provide physiological spinal loads and was bilaterally fused with pedicle screws and rods at L3-L4 spine levels. The exact geometry of the pedicle screw was simulated in this study for accurate stress prediction. The fused spine FE model was subjected to six loading directions (flexion/extension, left/right lateral bending, and left/right axial rotation). For each loading direction, a pure bending moment of 10 Nm was tested. First, FE analysis was performed for one loading cycle. Range of motion, maximum von Mises stress values of the spinal implants were recorded and compared for the six tested loading conditions. Then, based on the stress/strain history of the spinal implants for one loading cycle provided by the FE simulation, fatigue life cycles of the spinal implants were calculated using strain-based Smith-Watson-Topper equation. Flexion produced the largest range of motion at the adjacent level. Axial rotation produced the largest von Mises stress in the spinal implants. Except for lateral bending, the von Mises stress predicted in the screws fused at the superior vertebra was larger than that in the screws fused at inferior vertebra. The method introduced in this study will be used to study different screw fixation methods in the future work.

Commentary by Dr. Valentin Fuster
2017;():V001T02A047. doi:10.1115/DETC2017-67801.

Anterior Cruciate Ligament (ACL) injuries occur often in competitive sports such as soccer, basketball, football, and more. Athletes of all ages are at risk of experiencing this injury due to living highly active lifestyles. ACL injuries account for over $500 million in total medial cost in the United States, with about 150,000 annual occurrences of injury. Much research over this knee injury has been conducted as early as 1850, but confirmation of definite mechanisms of ACL injury have proved to be a difficult endeavor due to conflicting results found from experiments. Solving this problem could lead to implementation of preventative measures to help reduce to number of victims that undergo ACL injuries. The intention of this paper is to review the state-of-the-art of ACL injury research, including possible mechanisms of injury and the experimental methods used to analyze ACL performance.

Commentary by Dr. Valentin Fuster
2017;():V001T02A048. doi:10.1115/DETC2017-67901.

The way a person moves, either in a plain walk or performing a specific task, tells a huge quantity of information about his/her physical and, eventually, neurologic condition. A large part of a physiotherapist work of assessment is based on the qualitative evaluation, mainly visual, of a person’s movements, in terms of balance, speed, control, force and other parameters. This research work aims at providing personnel involved in the rehab process with a quantitative method to assess the way movements are performed. A numerical measure of the performance, actually, allows easier and more precise assessment, eliminating bias due to subjectivity. To accomplish this goal two steps are required: 3D acquisition of the movement using a Motion Capture (Mocap) system, and analysis of collected data to extract or elaborate the final outcome in the form requested by the medical staff. The paper shows the way Mocap acquisition are performed and data are analyzed in the application with people having a complete spinal cord injury and using a wheelchair. The method has been tested with eight volunteers in the rehabilitation department of the Hospital Papa Giovanni XXIII in Bergamo, Italy.

Topics: Wounds , Spinal cord
Commentary by Dr. Valentin Fuster
2017;():V001T02A049. doi:10.1115/DETC2017-67933.

This paper studies the knee joint dynamic strength modeling and simulation for squat lifting with heavy loads. The dynamic strength is modeled as a three-dimensional function of joint angle and velocity based on experimental isometric and isokinetic strength data. Then the dynamic strength function is formulated as joint torque limits in an inverse dynamics based optimization formulation. By using this formulation, squat lifting is predicted with heavy loads. An enumeration procedure is introduced to predict maximum lifting weight under various lifting conditions by considering dynamic strength of knee joint. In addition, the effect of lifting speed on the maximum lifting weight is investigated. Several future research directions are identified.

Topics: Stress , Knee
Commentary by Dr. Valentin Fuster
2017;():V001T02A050. doi:10.1115/DETC2017-67985.

Foot-ground interaction is modeled for a human gait simulation by using a 2D skeletal model with 12 degrees of freedom (DOF). Three contacting elements are attached to the heel, phalangeal, and toe sections respectively. The contacting process is modeled using an inverse optimization approach, in which the contacting force due to the penetration deformation and velocity is equal to the balanced ground reaction force (GRF). This is set as an equality constraint in the walking optimization formulation. A predictive dynamics approach is used to predict the walking motion and to optimize the contacting process. The results indicated that the contacting model can realistically match the GRF, and the resulting gait motion, contacting penetration, and contacting parameters are all optimized simultaneously. The optimal solution is obtained in seconds. This demonstrates an efficient way to model the foot-ground contacting deformation process using an inverse optimization method and eliminates the need for integrating equations of motion (EOM).

Topics: Optimization
Commentary by Dr. Valentin Fuster
2017;():V001T02A051. doi:10.1115/DETC2017-68064.

Cranioplasty is a procedure for skull reconstruction after removal of bone defects such as tumors. Recent approaches for cranioplasty involve the use of customized cranial implants (CCIs). A challenge in performing the cranioplasty with CCI is that the actual size/shape is unknown until the tumor is removed. Often the procedure is performed in two stages. After removing the cranial defect, the surgeon works with an implant manufacturer to develop a CCI using computer-aided design and manufacturing (CAD/CAM) techniques. The CCI attachment to the skull will then require a second surgery. We recently proposed a robot-assisted single-stage cranioplasty. For conventional, single-stage CCI, the CCIs are usually made in oversized profiles and require manual intraoperative modification by the surgeon. The challenge, however, is that for complex cases the surgeon may spend a long time reshaping the CCI. This paper presents the development of a 5-axis laser cutting machine that has the capability of automatically shaping CCI profiles during single-stage cranioplasty. Preliminary results indicate a superior fit with only mm size gaps between the implant and the remaining skull.

Topics: Laser cutting , Design
Commentary by Dr. Valentin Fuster
2017;():V001T02A052. doi:10.1115/DETC2017-68151.

Zero moment point (ZMP) is an important balance criterion for human motion planning. An important term in the ZMP formula is the rate of angular momentum (RAM) of each link. It is not trivial to compute this term compared to other terms in ZMP formula. In this paper, we first propose an efficient recursive Lagrangian method for calculating the rate of angular momentum in ZMP. This new approach gives a direct way to calculate the rate of angular momentum for each link. Secondly, we evaluate the effects of RAM in ZMP on human motion predictions for walking and running. These two motions are characterized as low speed and high speed motions respectively. We conclude that it is critical to include RAM in ZMP to predict accurate high speed motion. It has relatively less effect on low speed motion.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Knowledge Capture, Reuse, and Management

2017;():V001T02A053. doi:10.1115/DETC2017-67230.

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

Topics: Design
Commentary by Dr. Valentin Fuster
2017;():V001T02A054. doi:10.1115/DETC2017-67511.

Engineering designers often investigate patents as design precedents to learn about relevant prior arts, seek design inspiration, or assess the novelty of their own new inventions. However, patent retrieval relevant to the design of a specific product or technology is often unstructured and unguided, and resultant patents do not accurately capture the prior knowledge base. This paper proposes an iterative and heuristic methodology to comprehensively search for patents as precedents for the design of a specific technology or product. The methodology integratively mines patent text, citation and inventor information to identify relevant patents; in particular, the search keyword set, citation network, and inventor set are expanded through heuristic learning from the patents identified in prior iterations. The method relaxes the requirement for search keywords while improving patent retrieval accuracy. We apply the method to the identification of self-propelled spherical rolling robot patents. Engineers can use this repeatable method to retrieve patent sets representing the design precedents of their specialized interests.

Commentary by Dr. Valentin Fuster
2017;():V001T02A055. doi:10.1115/DETC2017-67562.

We hypothesize that by providing decision support for designers in industry we can speed up the design process and facilitate the creation of quality cost-effective designs. One of the challenges in providing design decision support is that the decision workflows embody various degrees of complexity due to the inherent complexity embodied in engineering systems. To tackle this, we propose a Knowledge-Based Platform for Decision Support in the Design of Engineering Systems (PDSIDES). PDSIDES is built on our earlier work that is anchored in modeling decision-related knowledge with templates using ontology to facilitate execution and reuse. In this paper, we extend the ontological decision templates to a computational platform that provides knowledge-based decision support for three types of users, namely, Template Creators, Template Editors, and Template Implementers, in original design, adaptive design, and variant design respectively. The efficacy of PDSIDES is demonstrated using a Hot Rod Rolling System (HRRS) design example.

Commentary by Dr. Valentin Fuster
2017;():V001T02A056. doi:10.1115/DETC2017-67817.

Utilizing the enterprise capital related the knowledge of design processes has become a crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design processes raise the challenge of capturing tacit knowledge and the ability to provide assistance in designing design processes. In this paper, an ontology is proposed for capturing, representing and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of Decision Support Problem Technique (DSPT), taking into account the requirements being able to guide assistance in designing design workflows, and integrating problem, product and process information in a design decision-making process. Then, the method of building procedure and design of process templates are presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

Topics: Design , Ontologies , Workflow
Commentary by Dr. Valentin Fuster
2017;():V001T02A057. doi:10.1115/DETC2017-67964.

The desire to use ever growing qualitative data sets of user generated content in the engineering design process in a computationally effective manner makes it increasingly necessary to draw representative samples. This work investigated the ability of alternative sampling algorithms to draw samples with conformance to characteristics of the original data set. Sampling methods investigated included: random sampling, interval sampling, fixed-increment (or systematic) sampling method, and stratified sampling. Data collected through the Vehicle Owner’s Questionnaire, a survey administered by the U.S. National Highway Traffic Safety Administration, is used as a case study throughout this paper. The paper demonstrates that existing statistical methods may be used to evaluate goodness of fit for samples drawn from large bodies of qualitative data. Evaluation of goodness of fit not only provides confidence that a sample is representative of the data set from which it is drawn, but also yields valuable real-time feedback during the sampling process. This investigation revealed two interesting and counterintuitive trends in sampling algorithm performance. The first is that larger sample sizes do not necessarily lead to improved goodness of fit. The second is that depending on the details of implementation, data cleansing may degrade performance of data sampling algorithms rather than improving it. This work illustrates the importance of aligning sampling procedures to data structures and validating the conformance of samples to characteristics of the larger data set to avoid drawing erroneous conclusions based on unexpectedly biased samples of data.

Commentary by Dr. Valentin Fuster
2017;():V001T02A058. doi:10.1115/DETC2017-68024.

A common way to react to mass customization and cost pressure is the introduction of modular product platforms in manufacturing companies. By the help of a modular product platform (MPP), which is composed of standardized and customizable product elements, it is possible to offer economically feasible prices for customized products. Although MPPs are commonly used across different industries, their advantages are not completely exploited. This is especially true when the reuse efficiency of standard elements and knowledge about its structures and elements are rather low. An explanation for the latter can be found looking at how product information is usually managed today. Product information is usually managed in a Product Lifecycle Management (PLM) system. However, current information technology (IT) solutions for PLM are not designed to systematically deliver platform information and support the lifecycle management of MPPs. Oftentimes, PLM solutions support product information for research and development. Thus, dealing with MPP’s lifecycle, which includes all relevant roles and adjusted permissions across the lifecycle, is often beyond the scope of PLM systems. Despite the severe industry need for improved information quality and availability on the entire MPP’s lifecycle, current research in this field lacks a systematic approach to tackle this problem. Theoretical foundations of an information structure for MPPs should be developed and platform specific requirements for PLM solutions need to be identified. It is shown how current PLM solutions can be adjusted, to support companies regarding MPP’s lifecycle management. Moreover, a detailed procedure to structure the information of MPPs is explained.

Therefore, this paper suggests a methodology to structure information of MPPs. While previous research papers contained an outline of the methodology to structure MPP’s information, this paper presents the detailed procedure. The methodology is based on the platform information flow, that is derived from a platform process model for the MPP’s lifecycle. Different perspectives and roles are considered to identify role specific information profiles and their access to information. Comparing different information profiles, information is characterized based on parameters to build the foundation of a common structure for a semantic information model. In this regard, the aim of this research is to increase efficiency for the business processes using a MPP, by means of a coherent structure for MPP’s information. That way, information can be stored and used by different roles along the platform’s business processes without creating redundancies or inconsistencies.

Commentary by Dr. Valentin Fuster
2017;():V001T02A059. doi:10.1115/DETC2017-68195.

Following the successful adoption of the open source model in the software realm, open source is becoming a new design paradigm in hardware development. Open source models for tangible products are still in its infancy, and many studies are required to demonstrate its application to for-profit product development. It is an alluring question why entrepreneurs decide to use an open model to develop their products under risks and unknowns, such as infringement and community management. The goal of this paper is to investigate the motivations of entrepreneurs of open source hardware companies. The leaders and founders of twenty-three companies were interviewed to understand their motivation and experiences in creating a company based on open source hardware. Based on these interviews, we generated a hierarchical framework to explain these motivations, where each level of the framework has been defined, explained and illustrated with representative quotes. The motivations of open source action are framed by two categories in the paper: 1) Intrinsic Motivation, which describes the motivations of an entrepreneur as an individual, who needs personal satisfaction, enjoyment as well as altruism and reciprocity; 2) Extrinsic Motivation, which describes motivations of an entrepreneur whose identity is as a for-profit company leader.

Topics: Hardware
Commentary by Dr. Valentin Fuster
2017;():V001T02A060. doi:10.1115/DETC2017-68451.

The goal of this paper is to explore how different modeling approaches to construct function structure models and different levels of model completion affect the information contained within the respective models. Specifically, the models are used to predict market prices of products. These predictions are compared based on their accuracy and precision. This work is based on previous studies on understanding how function modeling is done and how topological information from design graphs can be used to predict information with historical training. It was found that forward chaining was the least favorable chaining type irrespective of the level of completion. Backward chaining models work relatively better across all completion percentages, while Nucleation models don’t perform as well for a higher completion percentage. Hence, a greater attention is needed to understand and employ the methods yielding the most accuracy.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Methods, Processes and Strategies

2017;():V001T02A061. doi:10.1115/DETC2017-67240.

This paper investigates how and whether existing or current design tools, assist and support designers and engineers in the early-phases of ideation and conceptualization stages of design and engineering processes. The research explores how fluidly and/or congruously technology affords cognitive, emotive, gesture-based shape-and-form transformation and stimulates externalization within a hybrid design tool environment (HDTE). Meta-cognitive, emotive, gestural, sensorial, multi-dimensional interaction through exploration, translation and manifestation within a contextual blended environment is studied to enhance representation, stimulate choice-architecture and foster decision-making. Current and novel hybrid design tool developments and experiments illustrate the promise of hybridization for natural computing and unobtrusive design-tools (HDT) and cyber-physical systems (CPS). Put into perspective; a proposed framework of robust interaction design (IxD), gamification and affective computing (e.g. emotion) to improve and intensify user-experience (UX) and user-engagement (UE) is presented. The paper concludes by considering the allowance for possible novel routes to increase the scope and forging of links on prevailing frames of human-computer interaction (HCI).

Topics: Design
Commentary by Dr. Valentin Fuster
2017;():V001T02A062. doi:10.1115/DETC2017-67246.

Current and ongoing research and experimentations in the creation, design and build of low-cost, high-value prototypes for novel and unconventional interaction devices (IxD) in combination with cyber-physical system (CPS) (i.e. hybrid design tools (HDT), blended spaces) tangible user interfaces (TUI) and use of sensor technology lead to a variety of novel interaction modalities, experiences and possibilities. In line with this research, we propose a first prototype Human Sensor Selection Tool (HSST) as a preliminary guide and guidelines for design and engineering domains. The HSST is based on and inspired by the ‘five human senses’ [1], a plethora in human body signals (e.g. proprioceptive, vestibular) and gestures (e.g. facial expression, (e-)motions) that could be integrated, translated, transformed, adapted or mimicked to enhance and enrich the interaction modalities with for example computer-aided design (CAD), computer-aided technologies (CAx), and effectively affective CPS.

Topics: Design
Commentary by Dr. Valentin Fuster
2017;():V001T02A063. doi:10.1115/DETC2017-67560.

Cyber-physical systems enable new digital ecologies in industrial and workplace lifelong learning. This paper reports on early efforts in delivering a virtual environment and system for vocational education and training (VET), in particular targeting the needs of craft and trade skills. The domain of stone masonry is presented herein, where its underpinning activities are learning through virtual environments, simulation and role play. The challenges are not only the synchronicity between physical and software components but also the in-game mechanics that incorporate building blocks of effective training and skills development strategies. A prototype body-area sensor network in a cyber-physical game environment demonstrates the interaction between virtual objects and the player-learner.

Commentary by Dr. Valentin Fuster
2017;():V001T02A064. doi:10.1115/DETC2017-67738.

Immersive Virtual Reality (VR) systems such as the Oculus Rift or HTC Vive provide a sense of “presence” that is not available in traditional voice or video based communication methods. Without the sense of “presence” in the environment, a designer’s interpretation of the environment or design in question may be ill informed or skewed, based on the communication medium. The authors of this paper present a method to dynamically recreate a real-world environment in a virtual environment and provide an interface for physically-present individuals and geographically dispersed team members to collaborate. The method allows multiple remote users to naturally and immersively view a realistic representation of a dynamic real-world location in real time. This process incorporates consumer RGB-D sensors and VR systems into a distributed, multi-user virtual environment that has the ability to render large visual data in real-time. A case study using commodity RGB-D sensors, computing hardware, and standard TCP internet connections is presented to demonstrate the viability of the proposed method.

Commentary by Dr. Valentin Fuster
2017;():V001T02A065. doi:10.1115/DETC2017-67790.

This work wants to investigate which visualization method is able to support remote teleanalysis of industrial plants best regarding comprehension, usability and situation awareness. The application goal is the remote optimization of an industrial plant and the examined scenario was generated out of a large data set of a real production entity. The plant consists of an industrial manipulator, a molding machine and a montage system. Prior studies on the same plant with video based visualization explored by remote experts showed a large potential for optimization, but indicated a higher demand for situation awareness.

In order to test the influence of the visualization method, a user study has been carried out with 60 student participants with six different visualization methods, including various VR and AR implementations. Overall, our used AR environment performed significantly better than the used VR and video implementations, but the VR implementation surpasses AR regarding situation awareness.

Commentary by Dr. Valentin Fuster
2017;():V001T02A066. doi:10.1115/DETC2017-67850.

The advancement of in-vehicle technology for driving safety has considerably improved. Current Advanced Driver-Assistance Systems (ADAS) make road safer by alerting the driver, through visual, auditory, and haptic signals about dangerous driving situations, and consequently, preventing possible collisions. However, in some circumstances the driver can fail to properly respond to the alert since human cognition systems can be influenced by the driving context.

Driving simulation can help evaluating this aspect since it is possible to reproduce different ADAS in safe driving conditions. However, driving simulation alone does not provide information about how the change in driver’s workload affects the interaction of the driver with ADAS.

This paper presents a driving simulator system integrating physiological sensors that acquire heart’s activity, blood volume pulse, respiration rate, and skin conductance parameters. Through a specific processing of these measurements, it is possible to measure different cognitive processes that contribute to the change of driver’s workload while using ADAS, in different driving contexts.

The preliminary studies conducted in this research show the effectiveness of this system and provide guidelines for the future acquisition and the treatment of the physiological data to assess ADAS workload.

Commentary by Dr. Valentin Fuster
2017;():V001T02A067. doi:10.1115/DETC2017-67878.

Computer Aided Design (CAD) has been crucial to the engineering design process since the 1960s due to the ability to create 2D and 3D design representations and easily incorporate design changes without the cost of physical prototypes. However, CAD is hindered by its hardware displaying 3D objects as 2D representations, causing a loss of realism compared to a physical prototype. This paper seeks to observe if increasing the realism of interaction between subjects and virtual prototypes using virtual reality hardware will affect users ability to analyze the assembly model for errors. For this end, an experiment asked participants to perform a design review of assembly models and determine if they successfully form a cube without deformations or voids in four different environments. The environments ranged from low to high levels of immersion controlled through the use of two factors: movement of user’s point of view and assembly rotation. It is expected the highly immersive VR environment will allow for improved assembly reviews due to the increased realism of the virtual prototype.

Commentary by Dr. Valentin Fuster
2017;():V001T02A068. doi:10.1115/DETC2017-68228.

This paper presents an immersive virtual reality system (IVRS) that has been designed for unilateral amputees in order to reduce the phantom limb pain (PLP). The patient’s healthy limb is tracked by using a motion sensor. Data of the limb in motion are used as input parameters to move the phantom limb in the immersive virtual reality system. In this way, the patient has the illusion of moving the phantom limb while moving the real and contra-lateral limb. The system has been implemented by using low cost and open technologies, and combines the Oculus Rift SDK2 device, the LeapMotion device, a motion sensor, and an engine for interactive 3D content and gaming generation (Unity 3D). The Oculus Rift head mounted display is used to provide the immersive experience.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Simulation in Advanced Manufacturing

2017;():V001T02A069. doi:10.1115/DETC2017-67155.

Satisfied surface topography is important to achieve the function of a part, thereby machined surface prediction is essential. A surface forecasting model called space-time multioutput support vector regression (STMSVR) is developed in this paper. With machined surfaces pervading in manufacturing, high definition metrology (HDM) is adopted to measure the three dimensional machined surface. Millions of data points are generated to represent the entire surface. The STMSVR model captures the spatial-temporal characteristics of the successively machined surface and predicts the future surface. To verify the prediction accuracy of STMSVR, a case study on the engine cylinder block face milling process is applied. The results indicate that the developed model achieves a good agreement between the predicted surface and the real surface using four important indexes.

Commentary by Dr. Valentin Fuster
2017;():V001T02A070. doi:10.1115/DETC2017-67758.

A numerical analysis methodology, which demonstrates how a 1D pipework simulation can be enhanced with the results of a 3D CFD simulation of key components, is used to estimate the performance of multi-cylinder Positive Displacement pumps. The procedure uses of a 1-D lumped fluid dynamics model whose accuracy was improved by incorporating CFD analysis of the PD pump valves. The application describes how valve loss co-efficient resulting from CFD analysis was utilised by the lumped parameter model as an input function. The results suggest that the combination of the CFD and lumped parameter approach exceeds the limitations found by Iannetti [1] in modelling the interaction between the pump chambers of a multi-cylinder pump as the simplified lumped parameter approach makes the entire multi-cylinder model affordable in terms of computational power and time required. The results obtained are validated by means of experimental tests the results of which are presented together with the numerical data. An example of the capability of the procedure developed and the support it is able to provide to designers is also presented.

Commentary by Dr. Valentin Fuster
2017;():V001T02A071. doi:10.1115/DETC2017-67839.

The process of reducing the cross-section of a wire by pulling the wire through a series of dies belongs to a special class of manufacturing processes that have a sequential nature. In each pass, the diameter of incoming wire is reduced by a certain factor. Determining number of passes and their configurations required to achieve the desired reduction while optimizing properties such as tensile strength, strain distribution, energy consumption, etc. is an optimization problem. An essential building block of this optimization problem is a model of a drawing pass that can predict the output properties for a given parameter configuration of the pass (forward inference), or its inverse, i.e. predict the configuration parameters required to achieve the desired properties.

In this paper, we present a case study on the application of Bayesian networks to address the problem of inference in multi-pass wire drawing. Also we explore the building of a generic model that can be used for any pass and compare its effectiveness with pass-specific models. Our findings so far are as follows: For forward inference, the generic model has prediction accuracy close enough to pass-specific models. Given that it can be used to solve a problem with arbitrary number of passes, this model is clearly more useful. Further, this being a generative model, it can be used for inverse inference as well.

Topics: Wire drawing
Commentary by Dr. Valentin Fuster
2017;():V001T02A072. doi:10.1115/DETC2017-68134.

To investigate their in-plane dynamic response, a rigid plate with mass was given an initial velocity to impact (square) honeycombs in the X1 and X2 directions, respectively. Firstly, the impact model was built and validated. Then, impact resistance capacity research was conducted. Results showed that each honeycomb performed similarly in X1 and X2 directions, and the reentrant honeycomb usually used smaller displacement and time to absorb the same amount of kinetic energy. Thus, it is better for application if these factors were the main concerns. After that, the nominal stress at the proximal and distal ends were discussed under various impact velocities. It is shown that, under impact loading, the reentrant honeycomb generally showed higher initial peak stress as well as lower plateau stress at both proximal and distal ends. In addition, combining these with the deformation process of honeycombs, it was concluded that the formation of the plateau area of the nominal stress curve is related to the crushing displacement of the impact plate as well as the collapse of cells.

Commentary by Dr. Valentin Fuster
2017;():V001T02A073. doi:10.1115/DETC2017-68192.

Topology optimization provides optimized solutions with complex geometries which are often not suitable for direct manufacturing without further steps or post-processing by the designer. There has been a recent progression towards linking topology optimization with additive manufacturing, which is less restrictive than traditional manufacturing methods, but the technology is still in its infancy being costly, time-consuming, and energy inefficient. For applications in automotive or aerospace industries, the traditional manufacturing processes are still preferred and utilized to a far greater extent. Adding manufacturing constraints within the topology optimization framework eliminates the additional design steps of interpreting the topology optimization result and converting it to viable manufacturable parts. Furthermore, unintended but inevitable deviations that occur during manual conversion from the topology optimized result can be avoided. In this paper, we review recent advances to integrate (traditional) manufacturing constraints in the topology optimization process. The focus is on the methods that can create manufacturable and well-defined geometries. The survey will discuss the advantages, limitations, and related challenges of manufacturability in topology optimization.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Smart Manufacturing Informatics

2017;():V001T02A074. doi:10.1115/DETC2017-67143.

This paper addresses the combinatorial characterizations of the optimality conditions for constrained least-squares fitting of circles, cylinders, and spheres to a set of input points. It is shown that the necessary condition for optimization requires contacting at least two input points. It is also shown that there exist cases where the optimal condition is achieved while contacting only two input points. These problems arise in digital manufacturing, where one is confronted with the task of processing a (potentially large) number of points with three-dimensional coordinates to establish datums on manufactured parts. The optimality conditions reported in this paper provide the necessary conditions to verify if a candidate solution is feasible, and to design new algorithms to compute globally optimal solutions.

Topics: Cylinders , Fittings
Commentary by Dr. Valentin Fuster
2017;():V001T02A075. doi:10.1115/DETC2017-67652.

Capability analysis is a necessary step in the early stages of supply chain formation. Most existing approaches to manufacturing capability evaluation and analysis use structured and formal capability models as input. However, manufacturing suppliers often publish their capability data in an unstructured format. The unstructured capability data usually portrays a more realistic view of the services a supplier can offer. If parsed and analyzed properly, unstructured capability data can be used effectively for initial screening and characterization of manufacturing suppliers specially when dealing with a large pool of prospective suppliers. This work proposes a novel framework for capability-based supplier classification that relies on the unstructured capability narratives available on the suppliers’ websites. Naïve Bayes is used as the text classification technique. One of the innovative aspects of this work is incorporating a thesaurus-guided method for feature selection and tokenization of capability data. The thesaurus contains the informal vocabulary used in the contract machining industry for advertising manufacturing capabilities. An Entity Extractor Tool (EET) is developed for the generation of the concept vector model associated with each capability narrative. The proposed supplier classification framework is validated experimentally through forming two capability classes, namely, heavy component machining and difficult and complex machining, based on real capability data.

Commentary by Dr. Valentin Fuster
2017;():V001T02A076. doi:10.1115/DETC2017-67680.

As electronic devices and products are being miniaturized, the printed wiring boards (PWBs) within them are also being miniaturized. Therefore, it is becoming increasingly difficult to decide the drilling conditions required for producing small-diameter and high-density holes. We have been focusing on drilling conditions recommended in drill catalogs and have been attempting to gather knowledge that drilling experts use to decide the drilling conditions. In this study, we classify drills using the relationship between the diameter and the flute length and hence show that the methods used for setting the cutting conditions are different in different regions of a PWB. In addition, by using a catalog of microdrills that use alloy steel as the work material, we discuss how unique drilling conditions can be set for PWBs.

Commentary by Dr. Valentin Fuster
2017;():V001T02A077. doi:10.1115/DETC2017-67987.

The application of machine learning techniques in the manufacturing sector provides opportunities for increased production efficiency and product quality. In this paper, we describe how audio and vibration data from a sensor unit can be combined with machine controller data to predict the condition of a milling tool. Emphasis is placed on the generalizability of the method to a range of prediction tasks in a manufacturing setting. Time series, audio, and acceleration signals are collected from a Computer Numeric Control (CNC) milling machine and discretized into blocks. Fourier transformation is employed to create generic power spectrum feature vectors. A Gaussian Process Regression model is then trained to predict the condition of the milling tool from the feature vectors. We highlight that this multi-step procedure could be useful for a range of manufacturing applications where the frequency content of a signal is related to a value of interest.

Commentary by Dr. Valentin Fuster
2017;():V001T02A078. doi:10.1115/DETC2017-68186.

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Systems Engineering

2017;():V001T02A079. doi:10.1115/DETC2017-67257.

Defense in Depth (DiD) is a key design principle helping to improve the safety of complex systems in domains like nuclear power, oil and gas, and mining. DiD affects the basic design of the system because it contains requirements for isolation, diversity and safety divisions. If the DiD assessment happens late in the design process, there is a risk of costly redesign and project delays. To avoid this issue, this paper refines a set of early DiD assessment design rules and proposes a model-driven methodology for early assessment of the implementation of the DiD capabilities of a complex system design. The topology of the different design aspects of the system under study (mechanical, electrical, human factors, and others) and the dependencies between system elements are captured in a High Level Interdisciplinary Model (HLIM) that also holds DiD specific attributes. The resulting system model is assessed against the proposed set of DiD rules and requirements, and then it can be improved according to the results. The methodology is applied to a case study of an early nuclear power plant model of a spent fuel pool cooling system. The proof-of-concept software tool developed for early DiD assessment and presented in this paper is able to identify undesired dependencies between system elements of redundant systems, of different defense lines and other DiD related weaknesses. This provides practitioners with insights into potential vulnerabilities in the design and enables focused redesign to address the identified problems early in the design process.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Systems Engineering Information Knowledge Management

2017;():V001T02A080. doi:10.1115/DETC2017-67273.

Integrated vehicle simulation models are being increasingly used to improve engineering efficiency and reduce the number of real-world prototypes needed to understand vehicle attributes and subsystem interactions. Each domain within the vehicle must be represented by its own model developed with the appropriate operating ranges, behaviors, fidelity, and interfaces needed to interact appropriately with other domains in the vehicle. Planning and managing the development of these models across a large, multidisciplinary group of engineers can be a significant effort. In particular, carefully managing each model’s interfaces is crucial to enabling the entire process; missing or inappropriately used signals can cause significant issues when many separate domain models are integrated into a single vehicle model. To help system engineers better manage these interfaces across a broad variety of applications, a SysML-based modeling approach is proposed to describe these models and their interfaces formally and completely. However, even with a consistent modeling approach, creating and managing the interfaces across a large number of domains and applications can be a significant, error-prone task. To reduce the amount of manual modeling work required to maintain and update Simulink model interfaces, an interface management toolset is proposed to help automate the process of importing existing interfaces, routing and visualizing them, and exporting model templates for developers to use when creating new models. By automating this process, it becomes significantly easier to reuse models across vehicle platforms (rather than creating new models from scratch) and frees up resources to create more accurate simulations throughout the system design process.

Commentary by Dr. Valentin Fuster
2017;():V001T02A081. doi:10.1115/DETC2017-67930.

Requirements play very important role in the design process as they specify how stakeholder expectations will be satisfied. Requirements are frequently revised, due to iterative nature of the design process. These changes, if not properly managed, may result in financial and time losses leading to project failure due to possible undesired propagating effect. Current modeling methods for managing requirements do not offer formal reasoning necessary to manage the requirement change and its propagation. Predictive models to assist designers in making well informed decisions prior to change implementation do not exist. Based on the premise that requirement networks can be utilized to study change propagation, this research will allow for investigation of complex network metrics for predicting change throughout the design process. Requirement change prediction ability during the design process may lead to valuable knowledge in designing artifacts more efficiently by minimizing unanticipated changes due to mismanaged requirements. Two research questions (RQs) described are addressed in this paper:

RQ 1: Can complex network centrality metrics of a requirement network be utilized to predict requirement change propagation?

RQ 2: How does complex network centrality metrics approach perform in comparison to the previously developed Automated Requirement Change Propagation Prediction (ARCPP) tool?

Applying the notion of interference, requirement nodes in which change occurs are virtually removed from the network to simulate a change scenario and the changes in values of select metrics of all other nodes are observed. Based on the amount of metric value changes the remaining nodes experience, propagated requirement nodes are predicted. Counting betweenness centrality, left eigenvector centrality, and authority centrality serve as top performing metrics and their performances are comparative to ARCPP tool.

Commentary by Dr. Valentin Fuster
2017;():V001T02A082. doi:10.1115/DETC2017-67983.

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

Topics: Computers
Commentary by Dr. Valentin Fuster
2017;():V001T02A083. doi:10.1115/DETC2017-68013.

This paper describes an activity model that represents activities and information flow in dimensional metrology systems based on design information and measurement requirements from manufacturers. The purpose of developing the activity model is to facilitate measurement equipment selection rules and conformity decision rules development. The rules can be for users to plan a measurement process using functionally complex and highly capable dimensional measurement equipment and measurement software systems. This activity model provides a basis for developing a rule model as a part of the Quality Information Framework (QIF) standard.

Commentary by Dr. Valentin Fuster
2017;():V001T02A084. doi:10.1115/DETC2017-68105.

Storage tanks are constructed using thousands of large curved steel plates, which are manufactured from flat plates. In conventional manufacturing of curved plates, operators fit wooden templates to specific positions on steel plates and measure differences between the current shape and the template. However, it is costly to create many wooden templates for a variety of plates. In addition, it is time-consuming and requires skills to precisely place wooden templates on specific positions to measure differences. In this paper, we discuss methods to automatically calculate differences of shapes during bending processes without wooden templates. We capture dense points on steel plates using a terrestrial laser scanner, and analyze shapes of curved plates using point-clouds. In our method, the system extracts only the points on curved plates, and tracks the amount of deformation on reference lines defined on the plates. Corresponding positions between intermediate curved plates and the original flat shape are calculated using mesh flattening techniques. In our experiments, our method could calculate the amount of differences of steel plates in reasonable performance and precision.

Topics: Shapes , Storage tanks
Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Uncertainty Quantification in Simulation and Model Verification and Validation

2017;():V001T02A085. doi:10.1115/DETC2017-67438.

A methodology is proposed for uncertainty quantification to accurately predict the mechanical response of lattice structures fabricated by additive manufacturing. Effective structural properties of the lattice structures are characterized using a multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process, high resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling process facilitates obtaining of the homogenized strut properties of the lattice structure to reduce the computational cost of the detailed simulation model for the lattice structure. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also utilized to assess the predictive capability of the stochastic upscaling method used at strut level and lattice structure level. In comparison with physical compression tests, the proposed methodology of linking the uncertainty quantification with multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure by accounting for the uncertainties introduced by the additive manufacturing process.

Commentary by Dr. Valentin Fuster
2017;():V001T02A086. doi:10.1115/DETC2017-67556.

The present work describes the initial steps of developing a computational framework aiming to facilitate uncertainty quantification and propagation in non-linear computational models capable of predicting the dynamic response of blast-impacted human head assemblies. This work is motivated by the need to address the effects that the variables participating in such events have with respect to the severity and type of traumatic brain injury. Since high dimensional models are computationally very expensive, we have decided to incorporate model hyperreduction to enable the solution of the problem using reasonable computational resources, for the purpose of sampling the uncertainty of the parameters associated with the physics of the problem. We present an application of the adopted method for a simplified head assembly under blast-induced shock conditions, and a comparison between the high dimensional and hyperreduced results.

Commentary by Dr. Valentin Fuster
2017;():V001T02A087. doi:10.1115/DETC2017-68112.

Uncertainty is an integral part of decision making. While performing tradespace analysis multiple design alternatives need to be compared with respect to uncertain decision criteria in order to identify non-dominated design alternatives. However when the decision criteria is obtained from a computationally intensive numerical analysis or from an experimental analysis it might not be feasible to precisely derive distributions of the decision criteria for all design alternatives in the tradespace. In this study it is hypothesized that the availability of precise distributions of decision criteria for all design alternatives in the tradespace is not necessary and appropriate decisions can be made on the basis of imprecise distributions of decision criteria. Key contribution of this study is to investigate an approach using mean-risk analysis to sequentially evaluate a tradespace of design alternatives by bounding and sequentially reducing the imprecision in evaluation of experimental/numerical performance. A sequential decision process is presented where models of increasing fidelity are used to discriminate dominated design alternatives from the tradespace on the basis of imprecise distributions of decision criteria. Application of the framework is demonstrated on a multi-objective discrete choice problem of designing a two bar truss.

Topics: Risk
Commentary by Dr. Valentin Fuster
2017;():V001T02A088. doi:10.1115/DETC2017-68416.

Accurate modeling of the electrical behavior of a lithiumion (Li-ion) battery can provide accurate dynamic characteristics of the battery during charging/discharging and relaxation phases, which is essential to accurate online estimation of the battery state of charge (SoC). This paper proposes an ensemble bias-correction (BC) method with adaptive weights to improve the accuracy of an equivalent circuit model (ECM) in dynamic modeling of Li-ion batteries. The contribution of this paper is twofold: (i) the development of a novel ensemble method based on BC learning to model the dynamic characteristics of Li-ion batteries; and (ii) the creation of an adaptive-weighting scheme to learn online the weights of offline member BC models for building an online ensemble BC model. Repeated pulsing tests with single and multiple C-rates were conducted on seven Li-ion battery cells to evaluate the effectiveness of the proposed ensemble BC method. The analysis results with the use of an ECM demonstrate that the proposed method can reduce, on average, the voltage modeling error of the ECM by at least 50%.

Commentary by Dr. Valentin Fuster
2017;():V001T02A089. doi:10.1115/DETC2017-68429.

The temperature field model of steel slabs in a reheating furnace is pivotal in quality control of the steel rolling process. Computationally efficient models have been developed for online temperature control. However, accuracy of these models is often not satisfactory and thus causes quality control issues in the steel rolling process. This paper presents a model validation approach to improve accuracy of a temperature field model while maintaining its computational efficiency. Deterministic validation is firstly conducted by ignoring material property uncertainties through building a bias regression model with respect to different geometrical locations of a steel slab. With foreseeable accuracy improvement, statistical model validation is then further conducted by considering material property uncertainties so that reliable temperature control can be achieved for different batches of steel slabs in production.

Commentary by Dr. Valentin Fuster

37th Computers and Information in Engineering Conference: Virtual Environments and Systems

2017;():V001T02A090. doi:10.1115/DETC2017-67519.

One of the most challenging tasks throughout the development and manufacturing of a product is the capturing and formalization of engineering knowledge and expertise. In the past, many researchers have successfully proposed different techniques for capturing knowledge during the design, process and assembly planning of a product. However, few efforts have focused on applying knowledge capture to the task of product verification for Coordinate Measuring Machine (CMM) inspection; most of these are manual, obtrusive for the user and time consuming since the main sources of knowledge come from documentation such as handbooks, guides or interview transcripts. This paper describes a tool for the automated logging of a planner’s actions while carrying out an inspection planning task in a virtual CMM measurement environment. The tool involves a combination of 3D motion tracking and a post-processor to decipher the context strategy in the form of an inspection plan. Various representations of a captured strategy will benefit CMM operators by providing them a tool for: understanding planning strategies, better training methods for inexperienced users and producing more efficient part programs in a shorter time.

Commentary by Dr. Valentin Fuster
2017;():V001T02A091. doi:10.1115/DETC2017-67528.

To detect errors or find potential for improvement during the CAD-supported development of a complex technical system like modern industrial machines, the system’s virtual prototype can be examined in virtual reality (VR) in the context of virtual design reviews. Besides exploring the static shape of the examined system, observing the machines’ mechanics (e.g., motor-driven mechanisms) and transport routes for the material transport (e.g., via conveyor belts or chains, or rail-based transport systems) can play an equally important role in such a review. In practice it is often the case, that the relevant information about transport routes, or kinematic properties is either not consequently modeled in the CAD data or is lost during conversion processes. To significantly reduce the manual effort and costs for creating animations of the machines complex behavior with such limited input data for a design review, we present a set of algorithms to automatically determine geometrical properties of machine parts based only on their triangulated surfaces. The algorithms allow to detect the course of transport systems, the orientation of objects in 3d space, rotation axes of cylindrical objects and holes, the number of tooth of gears, as well as the tooth spacing of toothed racks. We implemented the algorithms in the VR system PADrend and applied them to animate virtual prototypes of real machines.

Commentary by Dr. Valentin Fuster
2017;():V001T02A092. doi:10.1115/DETC2017-68231.

This paper examines the fidelity of a commodity range camera for assembly inspection in use cases such as augmented reality-based assembly assistance. The objective of inspection is to determine whether a part is present and correctly aligned. In our scenario, shortly after the mechanics assembled the part, which is denoted as on-the-fly inspection. Our approach is based on object tracking and a subsequent discrepancy analysis. Object tracking determines the presence, position, and orientation of parts. The discrepancy analysis facilitates to determine whether the parts are correctly aligned. In comparison to a naive position and orientation difference approach, the discrepancy analysis incorporates the dimensions of parts, which increases the alignment fidelity. To assess this, an experiment was conducted in order to determine the accuracy range. The results indicate a sufficient accuracy for larger parts a noticeable improvement in comparison to the naive approach.

Commentary by Dr. Valentin Fuster

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