Microstructural variation of material directly affects the macroscopic and microscopic mechanical properties of the machined surface. This research investigates the correlation between microstructure evolution and the variation of microhardness and residual stress for cutting Ti-6Al-4V titanium alloy. Firstly, a 2D customized finite element (FE) model has been performed to predict dynamic recrystallization (DRX) and resultant grain size by implementing modified Johnson-Cook(J-C) constitutive model and Johnson-Mehl-Avrami-Kolmogorov(JMAK) model. Then, orthogonal cutting experiments are conducted to verify the accuracy of the established FE model. The predicted values agree well with the measured values and the average error is 8.94% for average grain size. Finally, microhardness and residual stress along with radial direction are measured. The results of simulation and experiment indicate that both microhardness and residual stress profile present an opposite correlation with average grain size profile under different cutting conditions. The correlation analysis between microstructural variation and macroscopic mechanical properties on the surface further deepens the understanding of the machined surface integrity system.

Complex curved thin-walled structures, mainly using multi-axis milling, are highly susceptible to deformation induced by residual stress. It is therefore that there is a considerable amount of researches on developing predictive models for machining-induced residual stress. However, these developed models for residual stress prediction mainly focus on turning and threeaxis milling. For multi-axis milling, a hybrid model combining experimental results and finite element(FE) model is established to predict the residual stress profile of Ti-6Al-4V titanium alloy in the current study. Based on the experimental and simulated results, the residual stress profile is fitted by the hyperbolic tangent function using the firefly algorithm (FA). Good fitting accuracy is obtained, which the R 2 values change from 85.3% to 99.1% in the σx direction and change from 80.7% to 98.1% in the σy direction. The radial basis function (RBF) neural network is used to establish the relationship between the coefficients of hyperbolic tangent model and the milling parameters. The prediction accuracy is verified to achieve 92.7% and 91.4% in the σx and σy directions, respectively. The effects of cutting speed, feed rate, and inclination angle on surface residual stress and influence depth are investigated. The results show that there is a strong nonlinear relationship between the surface residual stress and milling parameters. The proposed hybrid prediction model of residual stress can be used for further machining optimization of complex curved thin-walled structures.

A study of the response of a turbojet engine to the steady-state and the turbulence-type dynamic inlet distortion is presented in this paper. The steady-state distortion is generated by a 180° extent, 36 mesh screen, and the turbulence-type dynamic distortion by a 180° extent plate with 50% blockage ratio at the engine face. This plate can produce a very strong pressure fluctuation at the engine face. The statistical analysis shows that the APD of pressure fluctuation follows approximately the Normal Distribution except those cases near rotating stall or surge. Results from testing show: 1) inlet distortion generated by screen will produce a classical-surge or deep-surge (defined in ref. 1); 2) the degree of distortion by screen can change the mode of surge, e.g. from the classical-surge to the deep-surge and vice versa; 3) both the inlet distortion and the decrease in first-stage-turbine-nozzle area will change the compressor performance maps; 4) the turbulence-type dynamic distortion causes a “drift-surge” (defined in ref. 2).