Process Capability Sensitivity Analysis for Design Evaluation of Multistage Assembly Processes

• Published in: IEEE transactions on automation science and engineering Vol. 7; no. 4; pp. 736 - 745
• Main Authors: Huang, Wenzhen, Kong, Zhenyu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Assembly; Assembly lines; Automation; Automotive engineering; Calculus; Computer simulation; Derivatives; Design engineering; Design for quality; Design parameters; Finite difference methods; Manufacturing processes; Monte Carlo methods; Multistage; Parameter identification; process capability; Process controls; sensitivity; Sensitivity analysis; tolerance; Uncertainty; Yield estimation
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2009.2034633

Yield-based sensitivity analysis methods and algorithms are developed for process capability evaluation in this paper. Yield, the conformity to product specifications, is subject to critical design parameters such as dimensions, tolerances, and specification limits. The uncertainties in determining these parameters in design and manufacturing affect the process capability of producing high-quality products. Yield is a transparent and thus a desirable index, especially for multivariate process capability evaluation. Thus, yield sensitivity with respect to these design parameters indicates key contributors to final product quality, providing valuable information in design for quality control. Yield is formulated as a high dimension probability integral over a specification region. In multistage assembly processes, an assembly variation model links the quality characteristics to design parameters. This model is adopted in yield sensitivity analysis. Derivatives of yield with respect to the design parameters are developed using matrix calculus. Three sensitivity analysis algorithms, i.e., finite difference, yield derivative, and regression modeling, are implemented. Monte Carlo simulation is used for yield estimation in the three algorithms. A case study using floor pan assembly in automotive body manufacturing is presented for the validation of the proposed methodology.