Deep Transfer Learning for Improved Product Quality Prediction: A Case Study of Aluminum Gravity Die Casting

Abstract High product quality and low rejection rates are decisive for the competitiveness of manufacturing systems. Hence, manufacturing shifts towards digitalization for predicting and controlling product quality. In order to implement data-driven approaches in manufacturing successfully, sufficient model performances are necessary. Especially metal casting comes with several hurdles for data-driven approaches, like sparse and imbalanced data, a relatively high human interaction and frequent mounting events. This often leads to hardly generalizable and weak models. Within this paper, transfer learning is investigated for the quality prediction of aluminum gravity die casting to try to overcome these hurdles and create robust, accurate and data-efficient models.