Integrating Hybrid Physics-Data Approaches for Enhanced Cutting Force Modeling in Digital Twins of Helical End Mills

Abstract

Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and Machine Learning. Among these key technologies, digital twins stand out by offering a promising approach to intelligently utilize this data. In the virtual representation of a physical asset, data reflects the conditions of the physical entity, while models simulate and predict its behavior. In this paper, a hybrid cutting force model is proposed for digital twins of helical end mills, focusing on cutting force analysis during the utilization phase of the machining process. This model combines a fairly mature physical process modelling approach with a data-driven method, specifically a neural network trained on real process data, to address the limitations inherent in their respective applications. The physics-based model provides meaningful constraints on the neural network’s training, ensuring reliable cutting force prediction, particularly in scenarios with limited process data availability. The cutter’s profile, generated by the geometric model, and the cutter-workpiece engagement maps, derived from the virtual machining model, together serve as inputs for the hybrid cutting force model.