Optimizing mean and variance of multiresponse in a multistage manufacturing process using operational data

Abstract

A multistage process consists of sequential stages where each stage is affected by its preceding stage, and it in turn affects the stage that follows. The process described in this article also has several input and response variables whose relationships are complicated. These characteristics make it difficult to optimize all responses in the multistage process. We modify a data mining method called the patient rule induction method and combine it with desirability function methods to optimize the mean and variance of multiresponse in the multistage process. The proposed method is explained by a step-by-step procedure using a steel manufacturing process example.

Keywords:

• multistage process optimization
• desirability function
• data mining
• patient rule induction method
• robust parameter design
• mean and variance optimization
• multiresponse optimization

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2018R1D1A1B07049412]. This work was supported by the research fund of Hanyang University [HY-2019].

Notes on contributors

Dong-Hee Lee

Dong-Hee Lee is an Associate Professor in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. He received his BS and PhD in Industrial and Management Engineering from Pohang University of Science and Technology (Korea) in 2006 and 2011, respectively. He worked as a senior researcher in quality team of semiconductor division at Samsung Electronics for 4 years and received CRE (certified reliability engineer) from ASQ (American Society for Quality). His research interests include quality engineering methods such as statistical process control, design of experiments, statistical analysis, and so on. He has published several research articles about multiresponse surface optimization.

Jin-Kyung Yang

Jin-Kyung Yang earned her MS in the College of Interdisciplinary Industrial Studies in 2019 from Hanyang University in Korea. Her research includes statistical quality control, design of experiments, response surface methodology, and so on.

So-Hee Kim

So-Hee Kim is a MS candidate student in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. Her research includes statistical quality control, design of experiments, response surface methodology, and so on.

Kwang-Jae Kim

Kwang-Jae Kim is a Professor in the Department of Industrial and Management Engineering at Pohang University of Science and Technology, Korea. He earned his BS in Industrial Engineering in 1984 from Seoul National University, Korea, his MS in Industrial Engineering in 1986 from Korea Advanced Institute of Science and Technology, Korea, and his PhD in Management Science in 1993 from Purdue University. His research interests include quality assurance in product and process design, new product/service development, and service engineering. He is a member of ASQ, IIE, and INFORMS.