{"id":3274,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3274"},"modified":"2022-07-26T13:07:28","modified_gmt":"2022-07-26T11:07:28","slug":"predicting-the-solidification-time-of-low-pressure-die-castings-using-geometric-feature-based-machine-learning-metamodels","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/predicting-the-solidification-time-of-low-pressure-die-castings-using-geometric-feature-based-machine-learning-metamodels\/","title":{"rendered":"Predicting the solidification time of low pressure die castings using geometric feature-based machine learning metamodels"},"content":{"rendered":"\n

by Tobias Rosnitschek, Maximilian Erber, Bettina Alber-Laukant, Christoph Hartmann, Wolfram Volk, Frank Rieg, Stephan Tremmel (Germany)<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

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 trans- ferred 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.<\/p>\n\n\n\n

Keywords<\/strong>: Directed solidification; Casting design; Machine learning; Process assurance; Virtual product development<\/p>\n\n\n\n

<\/div>\n\n\n\n

Video presentation<\/strong><\/p>\n\n\n\n