Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process always has natural variations, it is crucial to detect significant changes to the process for quality control, as such changes may be the early signs of machine faults. This motivates our study on change-point detection methods for automotive manufacturing. We aim at developing a systematic approach for detecting process changes retrospectively in complex, nonstationary data. The proposed approach consists of nonparametric change-point detection, alarm generation based on change-point estimations, and performance evaluation against historical maintenance records. For change-point detection, three nonparametric methods are suggested—least absolute shrinkage and selection operator (LASSO), thresholded LASSO, and wild binary segmentation (WBS). Multiple decision rules are proposed to determine how to generate alarms from change-point estimations. Numerical studies are conducted to demonstrate the performance of the proposed systematic approach. The different change-point detection methods and different decision rules are evaluated and compared, with scenarios for choosing one set of change-point detection method and decision rule over another combination identified. It is shown that LASSO and thresholded-LASSO outperform WBS when the shift size is small, but WBS produces a smaller false alarm rate and handles the clustering of changes better than LASSO or thresholded LASSO. Data from an automotive manufacturing plant are used in the case study to demonstrate the proposed approach. Guidelines for implementation are also provided.
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July 2019
Research-Article
Manufacturing Process Monitoring With Nonparametric Change-Point Detection in Automotive Industry
Shenghan Guo,
Shenghan Guo
Department of Industrial and Systems Engineering,
Piscataway, NJ 08854
e-mail: sg888@scarletmail.rutgers.edu
Rutgers, The State University of New Jersey
,Piscataway, NJ 08854
e-mail: sg888@scarletmail.rutgers.edu
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Weihong (Grace) Guo,
Weihong (Grace) Guo
1
Mem. ASME
Department of Industrial and Systems Engineering,
Piscataway, NJ 08854
e-mail: wg152@rutgers.edu
Department of Industrial and Systems Engineering,
Rutgers, The State University of New Jersey
,Piscataway, NJ 08854
e-mail: wg152@rutgers.edu
1Corresponding author.
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Amir Abolhassani,
Amir Abolhassani
Global Data Insight and Analytics,
American Road, Dearborn, MI 48126
e-mail: aabolhas@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: aabolhas@ford.com
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Rajeev Kalamdani,
Rajeev Kalamdani
Global Data Insight and Analytics,
American Road, Dearborn, MI 48126
e-mail: rkalamda@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: rkalamda@ford.com
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Saumuy Puchala,
Saumuy Puchala
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: spuchala@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: spuchala@ford.com
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Annette Januszczak,
Annette Januszczak
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: ajanuszc@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: ajanuszc@ford.com
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Chandra Jalluri
Chandra Jalluri
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: cjalluri@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: cjalluri@ford.com
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Shenghan Guo
Department of Industrial and Systems Engineering,
Piscataway, NJ 08854
e-mail: sg888@scarletmail.rutgers.edu
Rutgers, The State University of New Jersey
,Piscataway, NJ 08854
e-mail: sg888@scarletmail.rutgers.edu
Weihong (Grace) Guo
Mem. ASME
Department of Industrial and Systems Engineering,
Piscataway, NJ 08854
e-mail: wg152@rutgers.edu
Department of Industrial and Systems Engineering,
Rutgers, The State University of New Jersey
,Piscataway, NJ 08854
e-mail: wg152@rutgers.edu
Amir Abolhassani
Global Data Insight and Analytics,
American Road, Dearborn, MI 48126
e-mail: aabolhas@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: aabolhas@ford.com
Rajeev Kalamdani
Global Data Insight and Analytics,
American Road, Dearborn, MI 48126
e-mail: rkalamda@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: rkalamda@ford.com
Saumuy Puchala
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: spuchala@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: spuchala@ford.com
Annette Januszczak
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: ajanuszc@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: ajanuszc@ford.com
Chandra Jalluri
Powertrain Manufacturing Engineering,
American Road, Dearborn, MI 48126
e-mail: cjalluri@ford.com
Ford Motor Company
,American Road, Dearborn, MI 48126
e-mail: cjalluri@ford.com
1Corresponding author.
Manuscript received July 3, 2018; final manuscript received April 12, 2019; published online May 28, 2019. Assoc. Editor: Satish Bukkapatnam.
J. Manuf. Sci. Eng. Jul 2019, 141(7): 071013 (23 pages)
Published Online: May 28, 2019
Article history
Received:
July 3, 2018
Revision Received:
April 12, 2019
Accepted:
April 15, 2019
Citation
Guo, S., Guo, W. (., Abolhassani, A., Kalamdani, R., Puchala, S., Januszczak, A., and Jalluri, C. (May 28, 2019). "Manufacturing Process Monitoring With Nonparametric Change-Point Detection in Automotive Industry." ASME. J. Manuf. Sci. Eng. July 2019; 141(7): 071013. https://doi.org/10.1115/1.4043732
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