0

Full Content is available to subscribers

Subscribe/Learn More  >

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

[+] Author Affiliations
Farhad Imani, Bing Yao, Ruimin Chen, Hui Yang

Pennsylvania State University, State College, PA

Prahalada Rao

University of Nebraska, Lincoln, NE

Paper No. MSEC2018-6523, pp. V003T02A003; 10 pages
doi:10.1115/MSEC2018-6523
From:
  • ASME 2018 13th International Manufacturing Science and Engineering Conference
  • Volume 3: Manufacturing Equipment and Systems
  • College Station, Texas, USA, June 18–22, 2018
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5137-1
  • Copyright © 2018 by ASME

abstract

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Copyright © 2018 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In