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Robust Performance Optimization Using Bayesian Optimization and Extreme Value Theory

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Abstract:

This work deals with Robust design optimization (RDO) under interval uncertainty and the resolution of such problems using a Bayesian optimization algorithm. In metal forming, process parameters such as tool radius, step size, or forming toolpath introduce variability that directly affects the final geometry and quality ofthe formed parts. In this context we aim at finding a design minimizing the amplitude of the performance interval but such a formulation does not account for the nominal performance. In this work, we introduce a scalarized objective adapted to the proposed algorithm allowing it to identify a Pareto optimum of both stability and nominal behavior. We propose an efficient expected improvement (EI) estimator for this objective based on an extreme-value approximation of surrogate extrema. The approach is illustrated on an analytical test problem and on a forming simulation with spring-back, where the new objective yields more practically relevant solutions than a variation-only robustness criterion.

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Periodical:

Key Engineering Materials (Volume 1049)

Pages:

141-150

DOI:

https://doi.org/10.4028/p-1kSPJP

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Cite this paper

Online since:

April 2026

Authors:

Augustin Persoons*, Valentin Duarte Rocha, Laurence Giraud-Moreau, Pascal Lafon

Keywords:

Active Learning, Bayesian Optimization, Forming Process, Optimization under Uncertainty, Robustness

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Creative Commons CC BY 4.0

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* - Corresponding Author

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