Machine learning for screw design in single‐screw extrusion

Abstract Artificial intelligence (AI) methods have significantly impacted various areas of technology, particularly in fields where large datasets are available. Screw designs are proprietary, and there is very limited information available in the open literature. In this study, we generated a dataset of 232 designs using computer simulation software for screw extrusion, involving solids transport, melting, and melt pumping. The parameters (features) and the outputs (targets) were introduced into four powerful machine learning (ML) algorithms. The capabilities of the four algorithms were assessed by comparing the predictions of each of the algorithms to the corresponding results of the simulations. Three of the algorithms demonstrated satisfactory performance, with the best‐performing one being further tested on an “unseen” dataset, which involved a screw of 75 mm and another of 127 mm in diameter. A machine‐learning technique called Permutation Feature Importance (PFI) was used to identify the features (parameters) with the greatest impact on the predictions. It is suggested that the same ML methodologies could be applied to datasets of existing real screw designs. Highlights Dataset obtained from simulation software. Four machine learning algorithms were employed. Assessment of algorithms based on training and testing data. Identification of parameters having greatest impact. Satisfactory predictions of mass flow rate, exit temperature, melting length, and more.