Predicting the solidification time of low pressure die castings using geometric feature-based machine learning metamodels

Titelangaben

Rosnitschek, Tobias ; Erber, Maximilian ; Alber-Laukant, Bettina ; Hartmann, Christoph ; Volk, Wolfram ; Rieg, Frank ; Tremmel, Stephan:
Predicting the solidification time of low pressure die castings using geometric feature-based machine learning metamodels.
In: Procedia CIRP. Bd. 118 (2023) . - S. 1102-1107.
ISSN 2212-8271
DOI: https://doi.org/10.1016/j.procir.2023.06.189

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Abstract

Casting process simulations are commonly used to predict and avoid defect formation. Their integration into structural optimization can enable automated structure- and process-optimized castings. Nevertheless, these simulations are time-consuming and computationally expensive. Therefore, this paper used graph theory and skeletonization techniques to extract geometric features from arbitrary 3D geometries and transferred them to machine learning-metamodels. This method can replace casting process simulation for the prediction of directional solidification in low-pressure die casting. Automated machine learning and hyperparameter optimization were used to systemize the search for well-suited neural network architectures. Two examples were used to train the metamodels, which are subsequently evaluated by a further test example, unknown to the training data and compared to the simulation results. The results showed an accuracy on unknown geometries over 60 and thus emphasized that neural network metamodels are capable of replacing time-consuming casting process simulation for specific objectives.