Taguchi Parameter Design with Multiple Quality Characteristics

Abstract

Taguchi parameter design is used extensively in industry to determine the optimal set of process parameters necessary to produce a product that meets or exceeds customer expectations of performance while minimizing performance variation. The majority of research in Taguchi parameter design has concentrated on approaches to optimize process parameters based on experimental observation of a single quality characteristic. This paper develops a statistical method, the DMT method, to evaluate and optimize multiple quality characteristic problems. The method incorporates desirability functions, a performance statistic based on the mean squared error, and data-driven transformations to provide a systematic approach that is adjustable to a variety of situations and easy for nonexperts to apply. This paper presents the DMT method in a step-by-step format and applies the method to two examples to illustrate its applicability to a variety of parameter design problems.

• DMT method
• desirability function
• mean squared error (MSE)

Additional information

Notes on contributors

Peter W. Phillips

Peter W. Phillips is a space systems project engineer, in the NOAA Operations, at the Aerospace Corporation, in Washington, D.C. His research interests are in the application of statistical quality control, probabilistic modeling, and design of experiments methodologies to the operation and maintenance of earth-orbiting satellites. He is a member of ASQ.

Phillips earned a master of science degree in industrial engineering and operations research from the Pennsylvania State University, and a master of science degree in management from Troy State University. He may be contacted at The Aerospace Corporation, 1000 Wilson Boulevard, Suite 2600, Arlington, VA 22209-3988; 301-457-5153; Fax [phone]; E-mail [email protected].

Kwang-Jae Kim

Kwang-Jae Kim is an associate professor of industrial engineering at Pohang University of Science and Technology in Pohang, Korea. His research interests are in the areas of quality in product and process design, statistical methods in quality assurance, new product development, and fuzzy set modeling in operations research. His research activities have been funded by various organizations, including Microsoft, IBM, LG Micron, and Korean Ministry of Science and Technology.

Kim's refereed journal articles include publications in Journal of Quality Technology, Quality Engineering, Quality Management Journal, Journal of Operations Management, Fuzzy Sets and Systems, European Journal of Operational Research, Computers and Operations Research, and Computers and Industrial Engineering. He is a member of INFORMS, ASQ, the Korean Institute of Industrial Engineers, the Korean Operations Research and Management Science Society, and the Korean Society for Quality Management.

Kim earned a Ph.D. degree in management from the Krannert Graduate School of Management at Purdue University. He may be contacted at the Department of Industrial Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang, Kyungbuk 790-784, Republic of Korea; 82-562-279-2208; Fax: [phone]; E-mail: [email protected].