Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming
Dworschak F, Sauer C, Schleich B, Wartzack S (2022)
Publication Type: Conference contribution
Publication year: 2022
Original Authors: Fabian Dworschak, Christopher Sauer, Benjamin Schleich, Sandro Wartzack
Series: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
City/Town: St. Louis, Missouri
Book Volume: ASME 2022
Conference Proceedings Title: Volume 2: 42nd Computers and Information in Engineering Conference (CIE)
Event location: St. Louis, MO
Abstract
This contribution presents an approach and a case study to compare Reinforcement Learning (RL) and Genetic Algorithms (GA) for parameter prediction in Sheet Bulk Metal Forming (SBMF). Machine Learning (ML) and Multi-Objective Optimization (MOO) to provide different points of view for the prediction of manufacturing parameters. While supervised learning depends on sufficient training data, GA lack the ability to explain how sufficient parameters were achieved. RL could help to overcome both issues, as it is independent from training data and can be used to learn a policy leading towards suitable parameter combinations, which can be tracked through the solution space. To probe RL in terms of feasibility for parameter prediction and necessary training effort SBMF serves as an appropriate use case because solution and objective function space are multidimensional, and their relations are challenging for MOO. The results of a Reinforcement Learner and a GA are compared and discussed to answer the question under which circumstances RL can provide an alternative for parameter prediction.
Authors with CRIS profile
Fabian Dworschak
Chair of Engineering Design
Christopher Sauer
Chair of Engineering Design
Benjamin Schleich
Chair of Engineering Design
Sandro Wartzack
Chair of Engineering Design
How to cite
APA:
Dworschak, F., Sauer, C., Schleich, B., & Wartzack, S. (2022). Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming. In The American Society of Mechanical Engineers (ASME) (Eds.), Volume 2: 42nd Computers and Information in Engineering Conference (CIE). St. Louis, MO, US: St. Louis, Missouri.
MLA:
Dworschak, Fabian, et al. "Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming." Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, St. Louis, MO Ed. The American Society of Mechanical Engineers (ASME), St. Louis, Missouri, 2022.
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