Abstract
Product performance varies with respect to time and space in many engineering applications. This paper discusses how to measure and evaluate the robustness of a product or component when its quality characteristics (QCs) are functions of random variables, random fields, temporal variables, and spatial variables. At first, the existing time-dependent robustness metric is extended to the present time-and space-dependent problem. The robustness metric is derived using the extreme value of the quality characteristics with respect to temporal and spatial variables for the nominal-the-better type quality characteristics. Then, a metamodel-based numerical procedure is developed to evaluate the new robustness metric. The procedure employs a Gaussian Process regression method to estimate the expected quality loss that involves the extreme quality characteristics. The expected quality loss is obtained directly during the regression model building process. Four examples are used to demonstrate the robustness analysis method. The proposed method can be used for robustness analysis during robust design optimization (RDO) under time-and space-dependent uncertainty.
Recommended Citation
X. Wei and X. Du, "Robustness Metric for Robust Design Optimization under Time-and Space-dependent Uncertainty through Metamodeling," Journal of Mechanical Design, vol. 142, no. 3, article no. 031110, American Society of Mechanical Engineers, Jan 2020.
The definitive version is available at https://doi.org/10.1115/1.4045599
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Available Access
Keywords and Phrases
Extreme value distribution; Gaussian process regression; Robust design; Robustness analysis; Time-and space-dependent uncertainty
International Standard Serial Number (ISSN)
1050-0472
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Society of Mechanical Engineers, All rights reserved.
Publication Date
01 Jan 2020
Comments
National Science Foundation, Grant 1923799