Bayesian closed-loop robust process design considering model uncertainty and data quality

Abstract

Response-surface-based design optimization has been commonly used in Robust Process Design (RPD) to seek optimal process settings for minimizing the output variability around a target value. Recently, the online RPD strategy has attracted increasing research attention, as it is expected to provide a better performance than offline RPD by utilizing online process feedback to continuously adjust process settings during process operation. However, the lack of knowledge about process model parameter uncertainty and data quality in the online RPD decisions means that this superiority cannot be guaranteed. This motivates this article to present a Bayesian approach for online RPD, which can provide systematic decisions of when and how to update the process model parameters for online process design optimization by considering data quality. The effectiveness of the proposed approach is illustrated with both simulation studies and a case study on a micro-milling process. The comparison results demonstrate that the proposed approach can achieve a better process performance than two conventional design approaches that do not consider the data quality and model parameter uncertainty.

Keywords:

• Bayesian analysis
• closed-loop approach
• data quality
• model uncertainty
• robust process design

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (grants 71702072, 71811540414, 71871119), the Natural Science Foundation for Jiangsu Institutions (grant BK20170810), the Fundamental Research Funds for the Central Universities (grant NR2019002), the Fujian Provincial Science and Technology Department (grant 2018J0176), and the international cooperation program managed by the National Research Foundation of Korea (grant 2018K2A9A2A06019662).

Notes on contributors

Linhan Ouyang

Linhan Ouyang is an associate professor in the College of Economics and Management at Nanjing University of Aeronautics and Astronautics, China. He holds a BEng degree in industrial engineering from Nanchang University, P.R. China, and a Ph.D. degree in management science and engineering from Nanjing University of Science and Technology, P.R. China. His research interests are process modeling and design of experiments.

Jianxiong Chen

Jianxiong Chen is an associate professor in the School of Mechanical Engineering and Automation at Fuzhou University, China. He holds a BEng degree in mechanical design, manufacturing and automation from Fuzhou University, P.R. China, and a Ph.D. degree in manufacturing and automation from Fuzhou University, P.R. China. His research interests are micro machining and motion control.

Yizhong Ma

Yizhong Ma is a professor in the Department of Management Science and Engineering, Nanjing University of Science and Technology, China. He received his B.S. in applied mathematics from Huazhong Normal University, China, and his MS in quality engineering and Ph.D. in control science from Northwestern Polytechnical University, China. He is also assigned as the Director of Quality Society of China, and the Expert Member of Six Sigma Promotion Committee in China. His research interests include quality engineering and quality management.

Chanseok Park

Chanseok Park started college as engineering student in the Department of Mechanical Engineering at Seoul National University and obtained a B.S. degree. He then received his M.A. in mathematics at University of Texas at Austin, and his doctorate in statistics at Pennsylvania State University. He is at present a professor of industrial engineering at Pusan National University, Korea. Before joining Pusan National University, he was a faculty member of Mathematical Sciences at Clemson University, Clemson SC, USA from 2001 to 2015. His research interests are quality control, reliability, and applied statistics.

Jionghua (Judy) Jin

Jionghua (Judy) Jin is a professor in the Department of Industrial and Operations Engineering at the University of Michigan. She received her Ph.D. degree from the University of Michigan in 1999. Her recent research focuses on data fusion and analytics for system monitoring, diagnosis, quality control, and decision making. Her research emphasizes a multidisciplinary approach by integrating applied statistics, machine learning, signal processing, reliability engineering, system control, and decision-making theory.