Research Papers

Design Improvement by Sensitivity Analysis Under Interval Uncertainty Using Multi-Objective Optimization

[+] Author and Article Information
J. Hamel

Department of Mechanical Engineering, University of Maryland, College Park, MD 20742hamel@umd.edu

M. Li

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, Chinamianli@sjtu.edu.cn

S. Azarm

Department of Mechanical Engineering, University of Maryland, College Park, MD 20742azarm@umd.edu

J. Mech. Des 132(8), 081010 (Aug 18, 2010) (10 pages) doi:10.1115/1.4002139 History: Received September 15, 2009; Revised June 25, 2010; Published August 18, 2010; Online August 18, 2010

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called design improvement by sensitivity analysis (DISA). DISA analyzes the interval uncertainty of input parameters and using multi-objective optimization, determines an optimal combination of design improvements that will ensure a minimal variation in the objective functions of the system, while also ensuring the feasibility. The approach provides a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. A two-stage sequential framework is used that can employ either the original analysis functions or their metamodels to greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness. The results produced by these examples show the ability of the approach to ensure the feasibility of a preexisting design under interval uncertainty by effectively adjusting available degrees of freedom in the system without the need to completely redesign the system.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Parameter uncertainty: (a) nominal and (b) reduced

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Figure 2

Parameter uncertainty mapping for various α values

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Figure 3

Example of parameter adjustment

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Figure 4

Graphical depiction of Rf(α) and Rg(α)

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Figure 5

The DISA approach: (a) without metamodels and (b) with metamodels

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Figure 6

Sample DISA results in two dimensions

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Figure 7

Sample propagation of DISA results

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Figure 8

β results for three sample αi∗ solutions

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Figure 9

Alternate problem DISA solutions

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Figure 10

Angle grinder system input-output

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Figure 11

Grinder β results for various αi∗ solutions



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