Abstract
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.
Highlights
Due to high potentials in reducing the weight of components, the integration of multi-material parts in the field of lightweight design is continuously increasing over the past years [1]
This paper proposes a novel approach applying Deep and Reinforcement Learning for the optimization of clinch joint characteristics
NSGA-II) in combination with labeled data, this contribution applies a Deep Reinforcement Learning algorithm to achieve the definition of an optimal clinching tool configuration based on a meta-model assisted data sampling process in order to answer three research questions (RQ)
Summary
Due to high potentials in reducing the weight of components, the integration of multi-material parts in the field of lightweight design is continuously increasing over the past years [1]. The implementation of machine learning methods in combination with a genetic algorithm (GA) showed already a high applicability to determine a solution space involving a wide range of possible design alternatives [3] Since this mainly requires the definition of a sufficient amount of training data, it is often time- and cost-intensive to setup powerful metamodels and to achieve a desired prediction accuracy. Few contributions already introduced different ways for a data-driven analysis of mechanical joining processes, an approach for the application of a semi-supervised machine learning algorithm to identify optimal clinch tool configurations is not available yet. Motivated by this lack, this paper proposes a novel approach applying Deep and Reinforcement Learning for the optimization of clinch joint characteristics. Concluding, the achieved results are summarized and an outlook provides further working steps
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