  Injection molding is one of the most popular manufacturing methods for the
modeling of complex plastic objects. Faster numerical simulation of the
technological process would allow for faster and cheaper design cycles of new
products. In this work, we propose a baseline for 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 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 baseline machine learning models for fill time and
deflection distribution prediction and provide baseline values of MSE and RMSE
metrics. Finally, we measure the execution time of our solution and show that
it significantly exceeds the time of simulation with Moldflow software:
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 this surrogate modelling
approach, we envision the use of trained models as a fast objective function in
the task of optimization of technological parameters of the injection molding
process (meaning optimal placement of gates), which could significantly aid
engineers in this task, or even automate it.
