A Multi-Physics Simulation Model for Universal Cutting Process based on an Enhanced CWE Extraction Method

Abstract

Cutting processes involve complex interactions among various physical factors that collectively influence machining performance, including cutting force, tool wear, deformation, and chatter. Accurately simulating these factors is essential for enhancing the efficiency of process development and optimization, yet it remains a significant challenge in the field. One of the main obstacles is the lack of a comprehensive simulation framework that integrates multiple physical models. To address this challenge, this paper presents a novel multi-physics simulation model that combines material removal, cutting force and temperature predictions, and tool wear distribution assessment. A key feature of our approach is the enhanced point-based Cutter-Workpiece Engagement (CWE) extraction algorithm, which accurately models cutting tools with arbitrary cutting-edge shapes and discretizes the cutting process into explicit orthogonal cutting elements. By breaking down complex time-varying processes into a series of standard problems, we can effectively integrate various physical factors. We utilize neural networks trained on physical datasets to derive cutting forces and temperatures for each element, facilitating precise predictions of tool wear evolution along the cutting edge throughout the machining process. The effectiveness of our method has been validated through ball-end milling experiments and an application of aeroengine blade milling process. This innovative, machine learning-integrated framework for multi-physics modeling establishes a solid foundation for a reliable and comprehensive virtual machining system.