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Injection molding is one of the most popular manufacturing methods for themodeling of complex plastic objects. Faster numerical simulation of thetechnological process would allow for faster and cheaper design cycles of newproducts. In this work, we propose a baseline for a data processing pipelinethat includes the extraction of data from Moldflow simulation projects and theprediction of the fill time and deflection distributions over 3-dimensionalsurfaces using machine learning models. We propose algorithms for engineeringof features, including information of injector gates parameters that willmostly affect the time for plastic to reach the particular point of the formfor fill time prediction, and geometrical features for deflection prediction.We propose and evaluate baseline machine learning models for fill time anddeflection distribution prediction and provide baseline values of MSE and RMSEmetrics. Finally, we measure the execution time of our solution and show thatit significantly exceeds the time of simulation with Moldflow software:approximately 17 times and 14 times faster for mean and median total timesrespectively, comparing the times of all analysis stages for deflectionprediction. Our solution has been implemented in a prototype web applicationthat was approved by the management board of Fiat Chrysler Automobiles andIllogic SRL. As one of the promising applications of this surrogate modellingapproach, we envision the use of trained models as a fast objective function inthe task of optimization of technological parameters of the injection moldingprocess (meaning optimal placement of gates), which could significantly aidengineers in this task, or even automate it.

Surrogate Modelling for Injection Molding Processes using Machine Learning