Rapid Finite Element Prediction on Machining Process

Finite Element Analysis (FEA) is widely used to simulate machining processes. However, in general, it is time consuming, error-prone, and requires repeated efforts to establish a verified successful Finite Element (FE) model. To rapidly investigate the effects of parameters such as tool angle, feed rate, cutting speed, and temperatures generated during the machining process, an efficient approach is proposed in this paper. The technique has been used to achieve rapid FF simulation during turning and milling processes using Python language programming of Abaqus. Sub-model 1 is programmed to simulate the chip formation process in Abaqus/Explicit. Sub-model 2 is programmed to simulate the cooling spring-back process by importing the machined surface into Abaqus/Implicit. The proposed method is capable of simulating the chip morphology, stress, strain and temperature of the machining process with different parameters immediately. The established FE models are automatically solved in batch by programming script. Post-processing is programmed by Abaqus script to easily achieve and evaluate the simulation results. The Programmed FE models are validated in terms of the predicted chip morphology, cutting forces and residual stresses. This method is extraordinarily efficient saving more than 33% simulation time in comparison to existing FEA approach used for machining processes. Moreover, the script is concise, easy to debug, and effectively avoiding interactive mistakes. The rapid programming model provides a novel, efficiency and convenient approach to thoroughly investigate the effects of a large number of parameters on machining processes.