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HomeKey Engineering MaterialsKey Engineering Materials Vol. 1050Multi-Objective Bayesian Optimization of...

Multi-Objective Bayesian Optimization of Dual-Phase Steel Microstructures for Minimal Damage Initiation

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Abstract:

Gaining a better understanding of the structure-property relationship in materials is a vital step in optimizing forming processes in order to minimize the induced damage and thereby maximizing the materials’ performance.Dual phase (DP) steels are comprised out of hard martensite surrounded by a soft and ductile ferrite matrix. Due to the complex microstructure of DP-steels, different mechanisms of damage initiation can occur, such as martensite cracking or ferrite-martensite phase boundary decohesion. A key problem with computational microstructure optimization focusing on one specific damage mechanism is, that this can lead to virtual microstructures, which are good against one mechanism, but vice-versa problematic for another mechanism. This is why all optimization strategies have to consider more than one mechanism. In this study, a multi-objective Bayesian optimization (moBo) approach is developed for the design of damage-tolerant DP-microstructures. It combines full-field crystal plasticity simulations on 3D representative volume elements with computational optimization. By employing the moBo, the sets of microstructure parameters are determined, where the combined minimum of both damage indicators is located. The proposed algorithm was applied to identify pareto-optimal microstructure configuration for DP800, considering both prevalent damage mechanisms It also provides an estimate of the variance associated with each parameter, which defines how critical the correct regulation of that aspect is. The results are in line with prevailing knowledge about DP steel, thus showing that the proposed approach is a promising tool for computational microstructure design

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Advanced Numerical and AI Strategies for Material Forming

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Periodical:

Key Engineering Materials (Volume 1050)

Pages:

21-30

DOI:

https://doi.org/10.4028/p-5MlW2P

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Cite this paper

Online since:

April 2026

Authors:

Irene Biermann*, Niklas Fehlemann, Maximilian Hribsek, Sebastian Münstermann

Keywords:

Bayesian Optimization, Dual-Phase Steel, Microstructure Optimization

Funder:

The publication of this article was funded by the RWTH Aachen University 10.13039/501100007210

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Creative Commons CC BY 4.0

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