Zirngibl C, Dworschak F, Schleich B, Wartzack S (2021)
Publication Type: Journal article, Original article
Publication year: 2021
DOI: 10.1007/s11740-021-01098-4
Open Access Link: https://doi.org/10.1007/s11740-021-01098-4
Due to increasing challenges in the area of lightweight design, the demand for time- and cost-effective joining technologies is steadily rising. For this, cold-forming processes provide a fast and environmentally friendly alternative to common joining methods, such as welding. However, to ensure a sufficient applicability in combination with a high reliability of the joint connection, not only the selection of a best-fitting process, but also the suitable dimensioning of the individual joint is crucial. Therefore, few studies already investigated the systematic analysis of clinched joints usually focusing on the optimization of particular tool geometries against shear and tensile loading. This mainly involved the application of a meta-model assisted genetic algorithm to define a solution space including Pareto optima with all efficient allocations. However, if the investigation of new process configurations (e. g. changing materials) is necessary, the earlier generated meta-models often reach their limits which can lead to a significantly loss of estimation quality. Thus, it is mainly required to repeat the time-consuming and resource-intensive data sampling process in combination with the following identification of best-fitting meta-modeling algorithms. As a solution to this problem, the combination of Deep and Reinforcement Learning provides high potentials for the determination of optimal solutions without taking labeled input data into consideration. Therefore, the training of an Agent aims not only to predict quality-relevant joint characteristics, but also at learning a policy of how to obtain them. As a result, the parameters of the deep neural networks are adapted to represent the effects of varying tool configurations on the target variables. This provides the definition of a novel approach to analyze and optimize clinch joint characteristics for certain use-case scenarios.
APA:
Zirngibl, C., Dworschak, F., Schleich, B., & Wartzack, S. (2021). Application of reinforcement learning for the optimization of clinch joint characteristics. Production Engineering. https://doi.org/10.1007/s11740-021-01098-4
MLA:
Zirngibl, Christoph, et al. "Application of reinforcement learning for the optimization of clinch joint characteristics." Production Engineering (2021).
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