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TECHNICAL PAPERS

Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction

[+] Author and Article Information
Hui Wang

Department of Industrial and Management Systems Engineering,  University of South Florida, Tampa, FL 33620

Qiang Huang1

Department of Industrial and Management Systems Engineering,  University of South Florida, Tampa, FL 33620huangq@eng.usf.edu

Reuven Katz

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

1

To whom all correspondence should be addressed.

J. Manuf. Sci. Eng 127(3), 512-521 (Aug 17, 2004) (10 pages) doi:10.1115/1.1948403 History: Received March 11, 2004; Revised August 17, 2004

Variation propagation modeling has been proved to be an effective way for variation reduction and design synthesis in multi-operational manufacturing processes. However, previously developed approaches for machining processes did not directly model the process physics regarding how fixture, and datum, and machine tool errors generate the same pattern on part features. Consequently, it is difficult to distinguish error sources at each operation. This paper formulates the variation propagation model using the proposed equivalent fixture error concept. With this concept, datum error and machine tool error are transformed to equivalent fixture locator errors at each operation. As a result, error sources can be grouped and root cause identification can be conducted in a sequential manner. The case studies demonstrate the model validity through a real cutting experiment and model advantage in measurement reduction for root cause identification.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

General 3-2-1 locating scheme and the FCS0

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Figure 2

EFE due to datum error

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Figure 3

(a) Machine tool error, (b) EFE due to machine tool

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Figure 4

Model derivation

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Figure 5

Raw workpiece and locating scheme (Unit: mm)

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Figure 6

Workpiece and locating

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Figure 7

Cutting Operations 1 and 2

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