Abstract
- One of the most extensively utilized techniques for material processing is injection molding, which is used to manufacture highly accurate and complex plastic items. In order to produce qualified items and ensure quality of the products, process parameter determination is crucial. We propose and compare two approaches for intelligently determining the parameters and controlling the quality within the injection molding process while aiming towards zero defects. These approaches are combinations of the process model trained using Automated Machine Learning (AutoML) with optimization techniques based on Bayesian and genetic algorithms.