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Research Papers: Design Automation

Robust Design for Multivariate Quality Characteristics Using Extreme Value Distribution

[+] Author and Article Information
Changming Yang

Professor
School of Mechanical Engineering
and Automation,
Xihua University,
Chengdu 610039, China
e-mail: cmyang@163.com

Xiaoping Du

Professor
Department of Mechanical
and Aerospace Engineering,
Missouri University of Science
and Technology,
400 West 13th Street,
Toomey Hall 290D,
Rolla, MO 65409
e-mail: dux@mst.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 21, 2013; final manuscript received June 17, 2014; published online July 31, 2014. Assoc. Editor: David Gorsich.

J. Mech. Des 136(10), 101405 (Jul 31, 2014) (8 pages) Paper No: MD-13-1534; doi: 10.1115/1.4028016 History: Received November 21, 2013; Revised June 17, 2014

Quality characteristics (QCs) are important product performance variables that determine customer satisfaction. Their expected values are optimized and their standard deviations are minimized during robust design (RD). Most of RD methodologies consider only a single QC, but a product is often judged by multiple QCs. It is a challenging task to handle dependent and oftentimes conflicting QCs. This work proposes a new robustness modeling measure that uses the maximum quality loss among multiple QCs for problems where the quality loss is the same no matter which QCs or how many QCs are defective. This treatment makes it easy to model RD with multivariate QCs as a single objective optimization problem and also account for the dependence between QCs. The new method is then applied to problems where bivariate QCs are involved. A numerical method for RD with bivariate QCs is developed based on the first order second moment (FOSM) method. The method is applied to the mechanism synthesis of a four-bar linkage and a piston engine design problem.

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