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

机译：双峰高斯曲线在加工IN718合金时残余应力分布的半经验预测

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摘要

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.
机译：在加工过程中，由于机械和热负荷，残余应力经常施加在最终产品上，从而影响变形和疲劳寿命。本文提出了一种原始的半经验方法来预测沿深度方向的残余应力分布。在该方法的统计模型中，创新地使用了双峰高斯函数来拟合从有限元模型获得的Inconel 718合金残余应力轮廓，从而实现了89.0％至99.6％的高拟合精度。利用随机森林算法对双峰高斯函数的系数与切削参数进行回归。回归精度控制在80％到85％之间，以防止过度拟合。进行了折衷圆柱车削和残余应力测量的实验，以修改有限元结果。实验修改后的有限元结果令人信服，确保了统计模型的合理性。结果表明，预测的残余应力与模拟一致，并且预测的数据点在误差线范围内。预测的表面残余应力（SRS）的最大误差为113.156 MPa，而最小误差为23.047 MPa。至于最大压缩残余应力（MCRS），最大误差为93.025 MPa，最小误差为22.233 MPa。考虑到Inconel 718的较大残余应力值，预测误差是可以接受的。根据半经验模型，研究了切削参数对残余应力分布的影响。结果表明，切削速度显着影响周向和轴向MCRS，周向和轴向沉降深度，因此对残余应力分布影响最大。同时，切削深度影响最小，因为它仅显着影响轴向MCRS和轴向沉降深度。

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