Adaptive quality prediction in injection molding based on ensemble learning
Despite the central role of quality, industry applications of data-based quality prediction for thermoplastics injection molding are rare, because of a suboptimal cost-benefit ratio. Therefore, we present a holistic approach for seamless part quality prediction, which automates the necessary data processing steps. Since the performance of the seven supervised learning algorithms applied with Bayesian hyperparameter-optimization depends on aspects such as process state, etc., we combine the learnt models using an ensemble-method to ensure good results under varying conditions. The results show that the ensemble’s performance significantly depends on the chosen ensemble-hyperparameters, so future research should focus on their automatic identification.
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