Combining Simulations and Machine Learning for Efficient Prediction of Process Parameters Evolution in Injection Moulding

In recent years, the emerging technologies in the context of Industry 4.0 have led to novel approaches in process monitoring and control, such as the introduction of Reinforcement Learning and Digital Twins. Consequently, large amounts of data, precise modelling and exhaustive simulations are required. The aim of this work is to propose a methodology based on the technique of backward selection to reduce the number of reference points in the simulation stage of manufacturing processes, enhancing the efficiency of data generation and the simplicity of the simulations. The methodology is proved in the particular case of plastic injection moulding simulations.