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TECHNICAL PAPERS

Using Error Equivalence Concept to Automatically Adjust Discrete Manufacturing Processes for Dimensional Variation Control

[+] 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

1

Correspondence author.

J. Manuf. Sci. Eng 129(3), 644-652 (Nov 13, 2006) (9 pages) doi:10.1115/1.2714581 History: Received August 26, 2005; Revised November 13, 2006

Traditional statistical process control (SPC) has been widely employed for the process monitoring in discrete part manufacturing. However, SPC does not consider any adjustment preventing the process drift. Furthermore, many in-line adjustment approaches, such as thermal error compensation and avoidance, are designed only for machine tool error reduction. This paper intends to fully utilize the engineering process information and to propose an alternative compensation strategy based on equivalent fixture error (EFE) concept that could reduce overall effect of the process errors. Considering three types of error sources in a machining process, we propose to adjust fixture locators to compensate errors using the EFE model. The dynamic property of EFE is investigated for the feedback adjustment of both static and quasi-static errors in machining processes. A minimum-mean-square-error controller is designed based on the dynamic EFE model. We then evaluate the performance of the controller such as stability and sensitivity. A self-updating algorithm for the controller will track the latest process information to stabilize the controller output. Finally, we simulate this process adjustment using the data collected from a real machining process. The results show that this algorithm can improve the machining quality.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 4

(a) Machine tool temperature and error data (15). (b) Thermal error measurements.

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Figure 6

Monitor for thickness and standard deviation of edge length

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Figure 7

Effect of controller parameters change

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Figure 3

Raw workpiece setup for a milling process (11)

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Figure 2

Process adjustment using EFE concept

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Figure 1

Process control and monitoring

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