Modeling of cutting force and tool vibration in helical milling using mechanistic models and artificial neural network

Helical milling is a hole enlarging process that makes holes with high quality and efficiency compared to traditional drilling. Since the power consumption is directly affected by the cutting forces, it is necessary to estimate the magnitude of the cutting forces and the amplitude of the cutter vibration in x, y and z directions with respect to chip geometry. The present study aimed to develop an intelligent manufacturing system to monitor the cutting forces while keeping the tool vibration in an acceptable range. Mechanistic models, finite element modeling (FEM) and artificial neural network (ANN) models have been developed to predict cutting forces and amplitude of cutter vibration related to chip geometry. Experiments were carried out on AISI 1020, AISI 4340 and AISI D2 steels at different levels of spindle rotation speed, orbital speed and axial depth of cuts using mill cutters with diameters of 8, 10 and 20 mm on DMC-75V linear three axes CNC vertical machining center. Experimental results for cutting force and amplitude of cutter vibration were measured and cutting force coefficients were calculated using the cutting force data and estimated cutting forces related to the chip geometry using mechanistic models. The ANN predicted the chip geometry, cutting forces and cutter vibration have good agreement with mechanistic approach. Among the three methods, the ANN is the simplest and took less time in estimation of chip geometry, cutting forces and amplitude of cutter vibration.