As one essential indicator of surface integrity, residual stress has an important influence on the fatigue performance of aero engines’ thin-walled parts. Larger compressive or smaller tensile residual stress is more prone to causing fatigue cracks. To optimize the state of residual stress, the relationship between the surface residual stress and the machining conditions is studied in this work. A radial basis function (RBF) neural network model based on simulated and experimental data is developed to predict the surface residual stress for multi-axis milling of Ti-6Al-4V titanium alloy. Firstly, a 3D numerical model is established and verified through a cutting experiment. These results are found to be in good agreement with average absolute errors of 11.6% and 15.2% in the σx and σy directions, respectively. Then, the RBF neural network is introduced to relate the machining parameters with the surface residual stress using simulated and experimental samples. A good correlation is observed between the experimental and the predicted results. The verification shows that the average prediction error rate is 14.4% in the σx direction and 17.2% in the σy direction. The effects of the inclination angle, cutting speed, and feed rate on the surface residual stress are investigated. The results show that the influence of machining parameters on surface residual stress is nonlinear. The proposed model provides guidance for the control of residual stress in the precision machining of complex thin-walled structures.

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).

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 three-axis 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 R2 values change from 85.3–99.1% in the σx direction and change from 80.7–98.1% 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.