On Nonlinear Empirical Modeling of Residual Stress Profiles in Hard Turning

With the development of advanced cutting tool materials, hard turning has become a beneficial alternative to grinding in finishing of highly stressed mechanical components. However, a poor selection of cutting parameters may cause deterioration of the component’s surface integrity and thus its fatigue life. Up to now, the control of surface integrity parameters is not possible as most of these quantities can not be measured during operation. Hence, suitable models are necessary to predict, e.g., the surface hardness and residual stresses induced by hard turning in-process. In order to achieve this, multiple regression and Takagi-Sugeno modeling are applied and compared in this contribution regarding their capability to predict axial and residual stress depth profiles depending on the cutting parameters and the initial hardness of the workpieces. Data from specimen machined on an industrial lathe and measured by X-ray diffraction are used for the case study.