Development of a machine learning-based design optimization method for crashworthiness analysis

Borse, A.; Gulakala, R.; Stoffel, M.

doi:10.24423/aom.4454

Artykuł - szczegóły

Tytuł artykułu

Development of a machine learning-based design optimization method for crashworthiness analysis

Autorzy

Borse A. , Gulakala R. , Stoffel M.

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DOI

10.24423/aom.4454

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Abstrakty

This article investigates design optimisation in the automotive field using machine learning (ML). A thin-walled crash box under axial impact is studied and the design parameters are optimised for front-impact crash tests. This study is based on geometrically and physically nonlinear shell theory, finite element analysis (FEA), dynamic buckling analysis and design optimisation using ML. An artificial neural network framework consisting of various ML methods is developed. A generative adversarial network is established for data generation and reinforcement learning is implemented to automate exploration of the design parameter. This ML framework is proven to determine optimal parameters under predefined crashworthiness constraints.

Słowa kluczowe

crashworthiness design generative adversarial network reinforcement learning nonlinear shell theory

Wydawca

Instytut Podstawowych Problemów Techniki PAN

Czasopismo

Archives of Mechanics

Rocznik

2024

Tom

Vol. 76, nr 1-2

Strony

61--92

Opis fizyczny

Bibliogr. 91 poz., rys., tab., wykr.

Twórcy

autor

Borse A.

borse@iam.rwth-aachen.de

Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany

autor

Gulakala R.

gulakala@iam.rwth-aachen.de

Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany

autor

Stoffel M.

stoffel@iam.rwth-aachen.de

Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany

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