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Residual stress plays a pivotal role in assessing the surface quality of machined components, influencing characteristics such as fatigue performance, corrosion resistance, and mechanical strength. Ti6Al4V, a critical material in the nuclear sector, the generation of residual stresses during machining has attracted significant attention. A model for variable friction coefficient model is proposed to accurately characterize the complex plastic deformation behavior of the material. This model takes into account variables such as the relative sliding velocity, contact pressure, and temperature. A subroutine has been developed using the VFRIC interface to enhance precise cutting simulation and modeling. Using the friction model, a new hybrid prediction model for surface residual stresses is developed by integrating finite element analysis and analytical methods. The reliability of both the hybrid and friction models is confirmed through the evaluation of cutting forces, chip morphology, and residual stress. The findings suggest that the novel friction model produces simulation results with higher accuracy compared to traditional Coulomb friction models. The variations in cutting force amount to 5.8% and 13.7%, the error in chip serration is 12.4%, and the deviation in predicting the maximum residual compressive stress is 10.3%. This study contributes to the advancement of friction models and methodologies used in predicting residual stress.

The constitutive model plays a decisive role in the accuracy and reliability of the simulation results. Because the material flow characteristics obtained by the traditional experimental method are difficult to describe thermoplastic deformation behavior in the cutting process accurately, and the inverse identification is usually carried out based on a two-dimensional orthogonal cutting model, ignoring the actual cutting state, which leads to significant deviations in the results of parameter identification. Therefore, a three-dimensional (3-D) turning finite element simplified model which conforms to the actual cutting scenario was established. Based on this model, a coupling simulation was used to inverse identify the Johnson-Cook (J-C) parameters for turning 304 stainless steel. Firstly, the automatic finite element modeling and calculation of 3-D turning is implemented by Python. Then, the inverse identification framework was built based on the ISIGHT. The initial yield strength of J-C parameters was obtained from quasi-static compression experiments. With the objective function of minimizing the error between the experimental cutting force and the simulated cutting force, the inverse identification of J-C parameters (strain reinforcement factor, strain rate sensitivity, thermal softening index, and hardening index) was carried out by a multi-island genetic algorithm. Finally, compared with cutting forces, chip morphology, and residual stresses, the feasibility of the inverse identification method and the accuracy of the constitutive model is demonstrated, which also provides a reference for the optimal identification method of constitutive parameters of difficult-to-machine materials.

Establishing a material constitutive model reflecting the mechanical behavior of metal materials in the cutting process is the basis of cutting finite element simulation, and has an important guiding role in accurately describing the cutting thermo-mechanical coupling state variables and further optimizing process parameters. This paper proposes a reverse identification method of material constitutive parameters based on co-simulation. The influence of mass scaling factor, mesh size, and Johnson-Cook (J-C) constitutive parameters on cutting force was analyzed, and orthogonal cutting experiments were carried out for two difficult-to-machine materials, die steel H13 and titanium alloy Ti-6Al-4V. ABAQUS is redeveloped based on Python, and then the J-C constitutive parameters are directly optimized for the first time with the minimum error between the finite element simulation value and the experimental value of the cutting force as the objective function, the least square identification of J-C constitutive parameters based on co-simulation is realized by combining genetic algorithm. The results show that the reasonable setting of mass scaling factor and mesh size can significantly reduce the time of simulation calculation and improve the optimization efficiency of constitutive parameters. The yield stress, hardening coefficient, strain rate coefficient, hardening coefficient and softening coefficient of materials have different effects on cutting force, which is the key to effective inverse identification of constitutive parameters. Compared with different J-C parameters reported in the literature, it is found that the constitutive parameters obtained by inverse identification based on co-simulation are more accurate, and the prediction of residual stress and serrated chip characteristics also has good accuracy. This study provides a reference for the optimization of constitutive parameters, and the research results provide a new scheme for the efficient construction of material mechanical behavior required for cutting finite element simulation.

The infiltration and cooling effect of lubricant droplets in the cutting zone largely depend on setting minimum quantity lubricant (MQL) system parameters appropriately. The study determines the optimal spray angle and spray distance relying on the geometric position relationship between the nozzle and the overall cutting zone. A numerical model that combines computational fluid dynamics (CFD) and discrete phase model (DPM) is presented to simulate droplet transport process for dynamic milling and one-tooth milling by flow field distribution and droplet characteristics. The influences of different spray distances and spray angles on the infiltration process and cutting tool temperature are analyzed. The experiments aimed at verifying the results of the analysis are conducted by measuring cutting force, cutting temperature, surface roughness, surface microtopography and tool wear. The result indicates that the spray distance has a significant impact on the jet divergence, and insufficient lubrication or excessive lubrication is effectively avoided with the optimal cutting distance. Spraying in the direction of cutting-out promotes the accessibility of the lubricant and greater concentration of droplets in the cutting zone, especially later in the cutting process, which results in increased infiltration, reduced friction, and improved heat dissipation. Moreover, experiments demonstate that the optimal state of jetting in the direction of cutting-out is 46 °C lower than that of dry cutting, the surface roughness is reduced by 19.8%, and the wear amount is reduced by roughly 34%. This study has a better understanding of the setting of spray distance and spray angle in MQL milling and the development of equipment, which provides theoretical guidance for the optimization of MQL system parameters in practical applications.

Finite element simulation is able to predict cutting force, temperature, stress distribution and chip formation. Constructing a constitutive model suitable for cutting simulation can accurately describe the state of the cutting process, which plays an important role in improving surface quality as well as optimizing cutting parameters and tool geometric design. Nickel-based superalloys are widely used in aerospace due to their excellent physical and mechanical properties at high temperatures. In this study, J-C parameters reverse identification of Inconel 718 alloy was carried out in both the solution annealed state and precipitation hardened state. The average cutting force and chip thickness were obtained by conducting orthogonal cutting experiments, after which the shear angle was calculated. Through quasi-static compression experiments, the yield strength, strain hardening modulus and hardening index of the material were then obtained. Considering the influence of the cutting-edge radius on cutting force during the cutting process, the Waldorf model was introduced into the analytical model in order to describe the deformation characteristics (strain, temperature, strain rate and stress) of the primary shear zone, after which the shear force was modified. The J-C constitutive parameters of the solution annealed state and precipitation hardened state were inversely identified by the intelligent optimization algorithm. According to the given range of cutting conditions, the accuracy of the method was verified through comparison with the cutting force and chip morphology of the finite element simulation. The differences in quasi-static and cutting dynamic mechanical responses of the two states were subsequently expounded. The effects of tool wear types and cutting parameters on the material flow characteristics at the tool tip as well as the thermal-mechanical load on the machined surface of precipitation hardened state Inconel 718 alloy were analyzed via numerical simulation. Accordingly, this study may provide insight into the identification of constitutive parameters of superalloys as well as the control of machined surface quality.