A cutting force prediction model for cutting tools with arbitrary cutting-edge distributions based on an enhanced cutter-workpiece engagement extraction method

Accurate prediction of cutting forces is crucial for optimizing machining processes, reducing costs, and shortening lead times. Cutter-workpiece engagement (CWE), representing the instantaneous contact geometry between the cutting edges and the in-process workpiece material, is indispensable for defining actual machining conditions and serves as a prerequisite for precise cutting force prediction. However, current approaches often oversimplify cutting-edge distributions by assuming uniform shapes to streamline computational efforts. Such simplifications inadequately capture complex geometries, compromising prediction accuracy and limiting applicability to diverse tool designs. To overcome these limitations, this study proposes a novel cutting force prediction model that accommodates tools with arbitrary cutting-edge geometries. The cutting edges are discretized into points independently of the tool contour to enable precise geometric characterization. An enhanced point-based algorithm is developed to determine CWE by decomposing the machining process into explicit oblique cutting elements. Cutting forces for these elements are predicted using an artificial neural network (ANN) trained on a dataset with labels derived from finite element simulations. The proposed method is validated through two meticulously designed experiments and a practical application in aeroengine blade milling. By bridging the gap between complex cutting-edge distributions and reliable force prediction, this work provides a customized and standardized framework for the efficient simulation of universal five-axis machining and advances the development of robust virtual machining systems capable of optimizing industrial processes.