Prediction of five-axis machining-induced residual stress based on cutting parameter identification

Abstract

The performance of the machined surface is significantly affected by the machining-induced residual stress (Rs), which should be well predicted for better regulation. However, the real-time factors, such as positioning error, and installation error, will make the actual cutting parameters (ACP) deviated from the designed cutting parameters (DCP), and decrease the Rs prediction accuracy. Thus, this paper proposes a novel cutting parameter identification method to improve the prediction accuracy of five-axis machining-induced residual stress. Firstly, the cutting parameter (the cutting width is used in this paper) is identified inversely by the real-time cutting force, which provides input parameters for the accurate Rs prediction. Then, the mechanical stress and the thermal stress are recalculated by the identified cutting parameters to improve the prediction accuracy. Finally, the loading conditions are determined by considering the effects of cutter postures, and the Rs prediction model is established in five-axis milling. Based on the experimental validation, the identified cutting parameters (ICP) are more closely to ACP. For example, the mean error of the identified cutting depth decreases from 0.075 mm to 0.03 mm, and the error rates of simulated temperature rise are significantly reduced by 68.8 %. The Rs prediction error rate obtained by ICP significantly decreases by 48.1 %. The proposed method improves the Rs prediction precision by inversely identifying the cutting parameter with the real-time cutting force. It benefits real-time control of Rs for the better surface quality of machined parts.