During the initial sampling of injection molds, the determination of suitable process parameter values to achieve a desired quality of the resulting parts, can be a time-consuming and demanding task. This is due to the complex viscoelastic properties of injection molding processes. Conducting technological investigations and using simulation techniques are popular approaches to support the design of the regarded process. However, while the former approach can require extensive research efforts, it can be difficult to design simulations and validate their prediction accuracy, especially when few process measurements are available as a baseline. In addition, the knowledge obtained by both, simulation and technologically based approaches, is only valid for the analyzed process configurations. In contrast, models based on machine learning (ML) approaches can provide forecasts for previously unseen data and can be evaluated quickly. Unfortunately, a high amount of data is required to train such models reasonably. In this contribution, a novel ML-based methodology to predict quality characteristics of an injection molding process for different process parameter values using an intelligent combination of simulation data and measurements, is presented.

在注塑模具的初始采样期间，确定合适的过程参数值以实现所得到零件的期望质量可能是一项耗时且苛刻的任务。这是由于注塑工艺复杂的粘弹性。进行技术调查和使用模拟技术是支持该过程设计的流行方法。但是，尽管前一种方法可能需要大量的研究工作，但可能难以设计仿真并验证其预测准确性，尤其是当很少有过程测量值可作为基准时。此外，通过仿真和基于技术的方法获得的知识仅对所分析的过程配置有效。相反，基于机器学习（ML）方法的模型可以为以前看不见的数据提供预测，并且可以快速进行评估。不幸的是，需要大量的数据来合理地训练这种模型。在此贡献中，提出了一种新颖的基于ML的方法，该方法使用模拟数据和测量值的智能组合来预测不同工艺参数值的注塑工艺的质量特性。