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Research Papers

Product Optimization Incorporating Discrete Design Variables Based on Decomposition of Performance Characteristics

[+] Author and Article Information
Masataka Yoshimura

Department of Aeronautics and Astronautics, Mechanical Engineering Division, Graduate School of Engineering, Kyoto University, Kyoto 606-8501, Japanyoshimura@prec.kyoto-u.ac.jp

Yu Yoshimura

Department of Aeronautics and Astronautics, Mechanical Engineering Division, Graduate School of Engineering, Kyoto University, Kyoto 606-8501, Japanyu.yoshimura@t02.mbox.media.kyoto-u.ac.jp

Kazuhiro Izui

Department of Aeronautics and Astronautics, Mechanical Engineering Division, Graduate School of Engineering, Kyoto University, Kyoto 606-8501, Japanizui@prec.kyoto-u.ac.jp

Shinji Nishiwaki

Department of Aeronautics and Astronautics, Mechanical Engineering Division, Graduate School of Engineering, Kyoto University, Kyoto 606-8501, Japanshinji@prec.kyoto-u.ac.jp

J. Mech. Des 131(3), 031004 (Feb 02, 2009) (10 pages) doi:10.1115/1.3066577 History: Received May 02, 2007; Revised November 04, 2008; Published February 02, 2009

This paper proposes a system optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics from original characteristics. The method is constructed to take advantage of hierarchical optimization procedures, enabling the incorporation of discrete design variables. The proposed method can be applied to machine product designs that include discrete design variables such as material types, machining methods, standard material forms, and specifications. The optimizations begin at the lowest levels of the hierarchical optimization structure and proceed to the higher levels. Discrete design variables are efficiently selected and optimized in the form of small suboptimization problems at the lowest hierarchical levels, and optimum solutions for the entire problem are ultimately obtained using conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems that include several types of discrete design variables are constructed, and applied examples are provided to demonstrate their effectiveness.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Decomposition of characteristics and hierarchical construction of suboptimization problems

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Figure 2

Transmission of Pareto solutions to upper levels

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Figure 3

Applied example model

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Figure 4

An example of frequency response at the cutting point of a machine-tool model

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Figure 5

Hierarchical optimization structure example for a simple machine-tool model

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Figure 6

Pareto optimum solution lines between the total structural member rigidity and the total material cost for alternative material types

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Figure 7

Conceptual diagram of Pareto optimum solution lines between the maximum surface roughness and the machining cost per unit of contact surface

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Figure 8

Optimum solution points between total structural member rigidity and total material cost for standardized material forms, and frontier Pareto optimum solution set

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Figure 9

SOP3 Pareto optimum solution line

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Figure 10

SOP3 Pareto optimum solution lines for standard material form selection

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Figure 11

SOP4 Pareto optimum solution line

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Figure 12

Optimization path for the hierarchical optimization structure shown in Fig. 5

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Figure 13

SOP1 Pareto optimum solution line

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