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Research Papers

Dynamic Optimization of the Grinding Process in Batch Production

[+] Author and Article Information
Cheol W. Lee

Department of Industrial and Manufacturing Systems Engineering, University of Michigan–Dearborn, 4901 Evergreen Road, Dearborn, MI 48128cheol@umich.edu

J. Manuf. Sci. Eng 131(2), 021006 (Mar 17, 2009) (9 pages) doi:10.1115/1.3090880 History: Received August 08, 2007; Revised January 10, 2009; Published March 17, 2009

This paper presents a novel dynamic optimization framework for the grinding process in batch production. The grinding process exhibits time-varying characteristics due to the progressive wear of the grinding wheel. Nevertheless, many existing frameworks for the grinding process can optimize only 1 cycle at a time, thereby generating suboptimal solutions. Moreover, dynamic scheduling of dressing operations in response to process feedback would require significant human intervention with existing methods. We propose a unique dynamic programming–evolution strategy framework to optimize a series of grinding cycles depending on the wheel condition and batch size. In the proposed framework, a dynamic programming module dynamically determines the frequency and parameter of wheel dressing while the evolution strategy locates the optimal operating parameters of each cycle subject to the constraints on the operating ranges and part quality. Case studies based on experimental data are conducted to demonstrate the advantages of the proposed method over conventional approaches.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Variables involved in the grinding process for batch production

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Figure 2

Relationship between pA and RA for 12.7≤ad≤50.8 and 0≤Vw′≤800

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Figure 3

Comparison of the conventional ES and the proposed DP-ES for varying batch sizes for the task of minimizing the production cost: (a) total production cost, (b) percent reduction in the total cost when switching from the ES to the DP-ES, and (c) number of required dressing operations

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Figure 4

Comparison of cost per cycle for a batch size of 23

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Figure 5

Comparison of the conventional ES and the proposed DP-ES for varying batch sizes for the task of minimizing the production time: (a) total production time, (b) percent reduction in the total time when switching from the ES to the DP-ES, and (c) number of required dressing operations

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Figure 6

Comparison of cycle times for a batch size of 16

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