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Research Papers

A Unified Framework for Integrated Optimization Under Uncertainty

[+] Author and Article Information
Zhonglai Wang

School of Mechanical and Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. Chinawzhonglai@uestc.edu.cn

Hong-Zhong Huang1

School of Mechanical and Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. Chinahzhuang@uestc.edu.cn

Yu Liu

School of Mechanical and Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China

1

Corresponding author.

J. Mech. Des 132(5), 051008 (May 17, 2010) (8 pages) doi:10.1115/1.4001526 History: Received January 23, 2009; Revised January 05, 2010; Published May 17, 2010; Online May 17, 2010

Reliability and robustness are two main attributes of design under uncertainty. Hence, it is necessary to combine reliability-based design and robust design at the design stage. In this paper, a unified framework for integrating reliability-based design and robust design is proposed. In the proposed framework, the probabilistic objective function is converted to a deterministic objective function by the Taylor series expansion or inverse reliability strategy with accounting for the probabilistic characteristic of the objective function. Therefore, with this unified framework, there is no need to deal with a multiobjective optimization problem to integrate reliability-based design and robust design any more. The probabilistic constraints are converted to deterministic constraints with inverse reliability strategy at the same time. In order to solve the unified framework, an improved sequential optimization and reliability assessment method is proposed. Three examples are given to illustrate the benefits of the proposed methods.

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Copyright © 2010 by American Society of Mechanical Engineers
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Flowchart of ISORA

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A single helical gear reducer

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Figure 1

Optimization under probabilistic objective function and constraints

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Figure 2

Optimization of the unified framework

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Figure 3

Inverse MPP search

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