Prediction of white layer thickness with machining time in milling of nickel-based superalloy Inconel 718 considering time-varying tool wear and tool temperature effect

Abstract

White layer has a significant impact on the surface quality of workpieces, so the accurate prediction of white layer thickness (WLT) is crucial. However, the present prediction models ignore the situation that WLT changes with machining time due to time-varying tool wear and tool temperature effect. Hence, a WLT prediction method during the milling process of Inconel 718 considering tool wear and tool temperature changes with machining time was presented in this paper. Firstly, a method was put forward to predict tool wear and tool temperature with machining time. Then, a finite element simulation model with obtained tool wear and tool temperature was set up to calculate WLT on the basis of dynamic recrystallization (DRX) mechanism. Finally, the prediction results were validated by the results of milling experiments. The average error of the proposed method was 12.36%. The results show the effectiveness of the proposed WLT prediction approach for Inconel 718, providing theoretical guidance for optimal machining time of each tool and cutting-parameter optimization.