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NEW OPPORTUNITIES AND BENEFITS IN THE PRODUCT DEVELOPMENT PROCESS USING THE MACHINE LEARNING BASED DIRECT INVERSE METHOD FOR MATERIAL PARAMETER IDENTIFICATION

Published online by Cambridge University Press:  19 June 2023

Paul Meißner  and
Thomas Vietor
Paul Meißner*
Affiliation:
Institute for Engineering Design, Technische Universität Braunschweig
Thomas Vietor
Affiliation:
Institute for Engineering Design, Technische Universität Braunschweig
*
Meißner, Paul, TU Braunschweig, Germany, [email protected]

Abstract

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Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

Keywords

Machine learningMaterial parameter identificationSimulationNeural NetworksComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2785 - 2794
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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