Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming

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.