Wenzel Manuel, Raisch Sven Robert, Schmitz Mauritius, Hopmann Christian
Corporate Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany.
Institute for Plastics Processing (IKV) in Industry and Craft at RWTH Aachen University, Seffenter Weg 201, 52074 Aachen, Germany.
Polymers (Basel). 2024 Aug 29;16(17):2465. doi: 10.3390/polym16172465.
Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.
机器学习（ML）方法为注塑成型过程的非线性行为建模提供了一个宝贵的机会。它们有潜力预测各种工艺和材料参数如何影响最终零件的质量。然而，注塑成型过程的动态特性以及与收集过程数据相关的挑战仍然是ML方法应用的重大障碍。为了解决这个问题，在本研究中，对将过程数据与其他过程知识（如本构方程和高保真数值模拟）相结合的混合方法进行了比较。混合建模方法包括特征学习、微调、增量建模、预处理和使用物理约束，以及各种方法的组合。为了训练和验证混合模型，使用了注塑零件的实验和模拟收缩数据。虽然所有混合方法都优于纯基于数据的模型，但在模拟设置中，微调方法产生了最佳结果。在实验设置中，校准物理模型（特征学习）并将其隐式纳入训练过程（物理约束）的组合优于其他方法。
