Dual-response optimization using a patient rule induction method

ABSTRACT

A dual-response surface optimization approach assumes that response surface models of the mean and standard deviation of a response are fitted well to experimental data. However, it is often difficult to satisfy this assumption when dealing with a large volume of operational data from a manufacturing line. The proposed method attempts to optimize the mean and standard deviation of the response without building response surface models. Instead, it searches for an optimal setting of input variables directly from operational data by using a patient rule induction method. The proposed approach is illustrated with a step-by-step procedure for an example case.

KEYWORDS:

• data mining
• dual-response surface optimization
• patient rule induction method
• process optimization
• response surface methodology

About the authors

Dong-Hee Lee is an Assistant Professor in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. He received his Ph.D. in Industrial and Management Engineering in 2011 from Pohang University of Science and Technology (Korea). 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 improvement methods such as response surface methodology, quality function deployment, and statistical process control and so on. He has published several research papers about multiresponse surface optimization.

Jin-Kyung Yang is a M.S candidate student in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. Her research includes statistical quality control, design of experiments, and response surface methodology and so on.

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 B.S. in Industrial Engineering in 1984 from Seoul National University, Korea, his M.S. in Industrial Engineering in 1986 from Korea Advanced Institute of Science and Technology, Korea, and his Ph.D. 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.

Additional information

Funding

This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (FY2017, 2016K2A9A2A11938390).