Bayesian online robust parameter design for correlated multiple responses

ABSTRACT

In production and manufacturing processes, noise factors are often considered difficult or costly to observe. The emergence of advanced sensor technology has made it easier for some major equipment to obtain large amounts of online monitoring data during the production stage of a product. In this paper, a new Bayesian approach is proposed to extend offline RPD to online multi-response RPD by making full use of this additional information. As new observations of the noise factor are obtained gradually, the settings of the controllable factors are adjusted online to further reduce the influence of noise factor variations on production quality. This approach not only addresses the correlation among multiple responses but also considers the uncertainty of model parameters and the variability of noise factors. A case study and a simulation study demonstrate that the proposed approach is superior to existing methods.

KEYWORDS:

• Seemingly unrelated regression
• Bayesian inference
• direct Monte Carlo
• time series model
• noise factor

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (NSFC) [grant numbers: 71771121, 71931006, 11801210], and the Postgraduate Research & Practice Innovation Program of Jiangsu Province [grant number: KYCX200344];Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_0344];

Notes on contributors

Shijuan Yang

Shijuan Yang is a Ph.D. candidate in Quality Management and Quality Engineering from Nanjing University of Science and Technology, China. Her research interests include applied statistics, Bayesian statistics, and quality management and quality engineering. E-mail: [email protected].

Jianjun Wang

Jianjun Wang is an Associated Professor of the Department of Management Science and Engineering at Nanjing University of Science and Technology. He is a member of QSR and INFORMS, and a senior member of Chinese Society of Optimisation, Overall Planning and Economical Mathematics. He is a reviewer of some famous international journals such as JQT, EJOR, IJPR, CAIE and QTQM. His research interests include parameter design  and optimisation, Bayesian statistics and modelling, industrial statistical and data analysis. E-mail: [email protected].

Xiaolei Ren

Xiaolei Ren is a Ph.D. candidate in Quality Management and Quality Engineering from Nanjing University of Science and Technology, China. Her research interests include quality management and quality engineering.  E-mail: [email protected].

Tingyu Gao

 Tingyu Gao received her M.S. degree in Management Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 2021. Her research interests include quality engineering. E-mail: [email protected].