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Research Papers

Engineering-Driven Factor Analysis for Variation Source Identification in Multistage Manufacturing Processes

[+] Author and Article Information
Jian Liu

Department of System and Industrial Engineering, The University of Arizona, Tucson, AZ 85712

Jianjun Shi1

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332

S. Jack Hu

Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109

1

Corresponding author.

J. Manuf. Sci. Eng 130(4), 041009 (Jul 10, 2008) (10 pages) doi:10.1115/1.2950064 History: Received April 06, 2007; Revised January 20, 2008; Published July 10, 2008

Variation source identification is an important task of quality assurance in multistage manufacturing processes (MMPs). However, existing approaches, including the quantitative engineering-model-based methods and the data-driven methods, provide limited capabilities in variation source identification. This paper proposes a new methodology that does not depend on accurate quantitative engineering models. Instead, engineering domain knowledge about the interactions between potential variation sources and product quality variables is represented as qualitative indicator vectors. These indicator vectors guide the rotation of the factor loading vectors that are derived from factor analysis of the multivariate measurement data. Based on this engineering-driven factor analysis, a procedure is presented to identify multiple variation sources that are present in a MMP. The effectiveness of the proposed methodology is demonstrated in a case study of a three-stage assembly process.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Overview of the proposed approach

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Figure 4

Conceptual illustration of indicator vector guided factor rotation

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Figure 8

A three-stage assembly process

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Figure 9

Graphical description of a three-stage assembly process

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Figure 10

Determination of the number of variation sources according to AIC

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Figure 11

Comparison of the rotated loading vectors with the true SPV

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Figure 12

Comparison of standardized SPVs

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Figure 13

Visualization of estimated SPVs

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Figure 6

Qualitative representation of quality/process interaction

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Figure 5

Graphical description of a MMP

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Figure 2

KPCs, variation source, and their SPVs

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Figure 1

Complex variation propagation scenario in a MMP

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Figure 7

Procedure of multiple variation source identification for a MMP

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