Forming process optimisation for variable geometries by machine learning – Convergence analysis and assessment

Abstract

Abstract. For optimum operation, modern production systems require a careful adjustment of the employed manufacturing processes. Physics-based process simulations can effectively support this process optimisation; however, their considerable computation times are often a significant barrier. One option to reduce the computational load is surrogate-based optimisation (SBO). Although SBO generally helps improve convergence, it can turn out unwieldy when the optimisation task varies, e.g. due to frequent component adaptations for customisation. In order to solve such variable optimisation tasks, this work studies how recent advances in machine learning (ML) can enhance and extend current surrogate capabilities. More specifically, an ML-algorithm interacts with generic samples of component geometries in a forming simulation environment and learns to optimise a forming process for variable geometries. The considered example of this work is blank holder optimisation in textile forming. After training, the algorithm is able to give useful recommendations even for new, non-generic geometries. While the prior work considered initial recommendations only, this work studies the convergence behaviour upon component-specific algorithm refinement (optimisation) at the example of two geometries. The convergence of the new pre-trained ML-approach is compared to classical SBO and a genetic algorithm (GA). The results show that initial recommendations indeed converge to the process optimum and that the speed of convergence outperforms the GA and compares roughly to SBO. It is concluded that – once pretrained –the new ML-approach is more efficient on variable optimisation tasks than classical SBO.