The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging 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 the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision 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 multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.
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April 2019
Research-Article
Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control
Farhad Imani,
Farhad Imani
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: fxi1@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: fxi1@psu.edu
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Bing Yao,
Bing Yao
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: bzy111@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: bzy111@psu.edu
Search for other works by this author on:
Ruimin Chen,
Ruimin Chen
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: rxc91@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: rxc91@psu.edu
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Prahalad Rao,
Prahalad Rao
Department of Mechanical and Materials Engineering,
Lincoln, NE 68588
e-mail: rao@unl.edu
University of Nebraska
,Lincoln, NE 68588
e-mail: rao@unl.edu
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Hui Yang
Hui Yang
1
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: huy25@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: huy25@psu.edu
1Corresponding author.
Search for other works by this author on:
Farhad Imani
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: fxi1@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: fxi1@psu.edu
Bing Yao
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: bzy111@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: bzy111@psu.edu
Ruimin Chen
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: rxc91@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: rxc91@psu.edu
Prahalad Rao
Department of Mechanical and Materials Engineering,
Lincoln, NE 68588
e-mail: rao@unl.edu
University of Nebraska
,Lincoln, NE 68588
e-mail: rao@unl.edu
Hui Yang
Department of Industrial and Manufacturing Engineering,
State College, PA 16802
e-mail: huy25@psu.edu
Pennsylvania State University
,State College, PA 16802
e-mail: huy25@psu.edu
1Corresponding author.
Manuscript received March 20, 2018; final manuscript received January 2, 2019; published online February 27, 2019. Assoc. Editor: Dragan Djurdjanovic.
J. Manuf. Sci. Eng. Apr 2019, 141(4): 044501 (7 pages)
Published Online: February 27, 2019
Article history
Received:
March 20, 2018
Revision Received:
January 2, 2019
Accepted:
January 2, 2019
Citation
Imani, F., Yao, B., Chen, R., Rao, P., and Yang, H. (February 27, 2019). "Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control." ASME. J. Manuf. Sci. Eng. April 2019; 141(4): 044501. https://doi.org/10.1115/1.4042579
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