Machining-induced residual stress plays significant role in the corrosion resistance and fatigue life of the manufacturing end-product. The high temperature condition in the turning process could induce microstructure changes of titanium alloys, which would directly influence the residual stress generation in the machining process. A prediction model is proposed to calculate the microstructure evolution in orthogonal turning process. Based on the explicit calculation of grain size growth and phase transformation, a microstructure-sensitive Johnson–Cook model considering the material microstructural attributes is developed for the machining-induced residual stress prediction of Ti-6Al-4V. Grain size characterization with electron backscatter diffraction test is conducted on the machined workpiece surface. Experimental measurement on force and residual stresses are conducted for model validations. The predicted force value agrees well with the measurement data. The general trend of residual stress profile is captured by the prediction model. The mechanisms of how the residual stress would be influenced by the different machining conditions, such as surface cutting speed, and feed rate, are also studied. The proposed method provides an in-depth understanding on the machined workpiece residual stress distribution as influenced by material microstructural attributes evolution in the machining process.

Machining-induced residual stress distribution is strongly influenced by the machining process condition, tool geometry and workpiece material mechanical properties. The high temperature, large strain and high strain rate environment will promote the material micro-structural attribute changes. The material micro-structural attribute changes could directly affect the material mechanical properties. An analytical model is proposed for the residual stress prediction in the orthogonal turning by considering the material dynamical recrystallization induced grain growth effect. The grain size effect on the material flow stress behavior is included by adding a grain size dependent term into the traditional Johnson–Cook model. The Johnson–Mehl–Avrami–Kolmogorov model calculates the recrystallized volume fraction and grain size as a function strain, strain rate and time. The average grain size is calculated with a rule of mixture by volume. Then the modified Johnson–Cook model is embedded into a classic residual stress prediction model for the machining induced residual stress profile prediction on the machined workpiece surface. Experimental tests are conducted for the model validation. The predicted residual stress shows good approximation with the measurement in both the trend and magnitude of the residual stress. Also, the effects of cutting speed and feed rate on the residual stress profile are investigated.

The material microstructure attributes are largely ignored in the machining community for the machining mechanics modeling. A physical-based mechanical threshold stress (MTS) model is proposed for the orthogonal turning application of Ti-6Al-4V material. The MTS model takes the material internal state variables, such as dislocation to dislocation interaction and dislocation/interstitial resistance, into the flow stress consideration. The MTS model is embedded into an analytical residual stress prediction model for machining induced residual stress prediction. The experimental data are provided for the model validation. The prediction generally captures the trend of the residual stress profile compared with experiments. The proposed model provides a microstructure insight of the workpiece material in the machining process modeling.