关键词: 纤维增强复合材料, 螺旋铣, 切削力建模, 纤维切削方向角, 人工神经网络

Abstract: The helical milling process has the advantages of reducing the axial force and improving the chip removal and heat dissipation conditions. One of the important process indicators is the helical milling force. In this paper, the modeling method of unidirectional CFRP helical milling force is studied to predict the helical milling force with given machining parameters. First of all, through kinematics analysis and chip geometry analysis of the helical milling process, the side edge and bottom edge dynamic chip thickness models, the fiber cutting direction angle model, and the dynamic cutting force calculation model of the process are established. The cutting force coefficients are then calibrated through the linear groove milling experiment and the bottom edge half-teeth gear milling experiment, respectively, and fitted by the artificial neural network. Finally, the calibrated cutting force coefficients are introduced into the dynamic cutting force prediction model, thereby establishing the unidirectional CFRP helical milling dynamic cutting force prediction model. The accuracy of this model is subsequently verified through the experiment. Compared with the existing model, this model can predict both the change of the cutting force in the spiral motion cycle and the details of each tool rotation cycle. Considering the influence of the fiber cutting direction angle on the cutting force coefficient, it reflects the anisotropy of the unidirectional CFRP material, therefore more accurately predicting the spiral milling force.

Key words: fiber reinforced composites, helical milling, cutting force modeling, fiber cutting direction angles, artificial neural networks