Semi-Empirical Prediction of Residual Stress Profiles in Machining IN718 Alloy Using Bimodal Gaussian Curve

Residual stresses are often imposed on the end-product due to mechanical and thermal loading during the machining process, influencing the distortion and fatigue life. This paper proposed an original semi-empirical method to predict the residual stress distribution along the depth direction. In the statistical model of the method, the bimodal Gaussian function was innovatively used to fit Inconel 718 alloy residual stress profiles obtained from the finite element model, achieving a great fit precision from 89.0% to 99.6%. The coefficients of the bimodal Gaussian function were regressed with cutting parameters by the random forest algorithm. The regression precision was controlled between 80% and 85% to prevent overfitting. Experiments, compromising cylindrical turning and residual stress measurements, were conducted to modify the finite element results. The finite element results were convincing after the experiment modification, ensuring the rationality of the statistical model. It turns out that predicted residual stresses are consistent with simulations and predicted data points are within the range of error bars. The max error of predicted surface residual stress (SRS) is 113.156 MPa, while the min error is 23.047 MPa. As for the maximum compressive residual stress (MCRS), the max error is 93.025 MPa, and the min error is 22.233 MPa. Considering the large residual stress value of Inconel 718, the predicted error is acceptable. According to the semi-empirical model, the influence of cutting parameters on the residual stress distribution was investigated. It shows that the cutting speed influences circumferential and axial MCRS, circumferential and axial depth of settling significantly, and thus has the most considerable influence on the residual stress distribution. Meanwhile, the depth of cut has the least impact because it only affects axial MCRS and axial depth of settling significantly.