A novel surface residual stress monitoring method based on the power consumption of machine tool: A case study in 5-axis machining

Abstract

The machining-induced residual stress significantly influences the service performance of machined parts and should be well monitored. However, the direct measurement method is time-consuming and only can be conducted after machining. Meanwhile, the existing prediction method rarely considers the effects of real-time factors on the machining parameters and cannot meet the requirement of online residual stress monitoring. Therefore, a novel surface residual stress monitoring method is proposed based on the easily measured online factor (power consumption of the 5-axis machine tool is used in the paper). Firstly, the power consumption of the machine tool (Pt) is used to model the mean effective cutting power (Pe¯). Secondly, the relationship between the surface residual stress in the feed direction (Rsx) and Pe¯ is established. Thirdly, Rsx is predicted based on Pt by combining these two models. Finally, a novel surface residual stress monitoring method based on Pt is proposed. The effectiveness of the proposed method is validated by various experiments, and the mean prediction error rate is only 9.5 %. From the case study, when the power consumption of machine power increases at a fixed spindle speed, Rsx changes from tensile stress to compressive stress. It provides a new method and platform for controlling the surface residual stress and greatly benefits the high-performance manufacturing of complex parts.