Surrogate modeling for injection molding processes using deep learning

• Published in: Structural and multidisciplinary optimization Vol. 65; no. 11
• Main Authors: Uglov, Arsenii, Nikolaev, Sergei, Belov, Sergei, Padalitsa, Daniil, Greenkina, Tatiana, Biagio, Marco San, Cacciatori, Fabio Massimo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022; Springer Nature B.V
• Subjects: Algorithms; Applications programs; Automobile industry; Computational Mathematics and Numerical Analysis; Computer simulation; Data processing; Deep learning; Deflection; Engineering; Engineering Design; Error analysis; Industrial Application Paper; Injection molding; Machine learning; Manufacturing; Mathematical models; Optimization; Process parameters; Product development; Production methods; Theoretical and Applied Mechanics; Deep learning; 3d machine learning; Point cloud; Machine learning; Injection molding; Fluid dynamics simulation; Mesh; Surrogate modeling; 3d data; Autodesk Moldflow
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-022-03380-0

Injection molding is one of the most popular manufacturing methods for making complex plastic objects. Faster numerical simulation of this manufacturing process would allow faster and cheaper design cycles of new products. In this work, we propose a data processing pipeline that includes the extraction of data from Moldflow simulation projects and the prediction of the fill time and deflection distributions over 3-dimensional surfaces using machine learning models. We propose algorithms for the engineering of features, including information of injector gates parameters that will mostly affect the time for plastic to reach the particular point of the form for fill time prediction, and geometrical features for deflection prediction. We propose and evaluate machine learning models for fill time and deflection distribution prediction and provide values of Mean Absolute Error, Median Absolute Error, and Root Mean Square Error metrics. Finally, we measure the execution time of our solution and show that our solution is much faster than Moldflow: approximately, 17 times and 14 times faster for mean and median total times, respectively, comparing the times of all analysis stages for deflection prediction. Our solution has been implemented in a prototype web application that was approved by the management board of Fiat Chrysler Automobiles and Illogic SRL. As one of the promising applications of similar surrogate modeling approaches, we envision the use of trained models as a fast objective function for optimizing injection molding process parameters, such as optimal placement of gates, which could significantly aid engineers in this task, or even automate it.