A prediction method of tool wear distribution for ball-end milling under various postures based on WVEM-T

The contact positions corresponding to various tool location point during ball-end milling are complex, and the actual cutting area of flank face presents uneven wear form, which is closely related to its effective cutting distance, linear velocity of edge line microelement, and instantaneous undeformed chip thickness, etc. It is difficult to accurately predict the actual tool wear distribution by theoretical modeling. Therefore, it is necessary to put forward a prediction method of tool wear distribution to ensure the quality of workpiece and the stable state of tool during machining. In this paper, the effective cutting length of tool edge line microelement is calculated, and the instantaneous undeformed chip thickness under various postures considering edge wear is determined. A weighted voting ensemble multi-Transformer transfer learning (WVEM-T) model is established, motion parameters and the actual wear widths VB per edge line are used as training data. The selective freezing strategy is adopted to update the training parameters of the network, so that the trained multi-layer network can accurately predict the wear distribution of flank face in ball-end milling tool under various machining inclination angles. Finally, the accuracy and effectiveness of the prediction method in this paper are verified by the whole life cycle experiment of milling Ti6Al4V alloy.