In order to improve the wear and corrosion resistance of an AZ91D magnesium alloy substrate, an Al_0.5CoCrCuFeNi high-entropy alloy coating was successfully prepared on an AZ91D magnesium alloy surface by laser cladding using mixed elemental powders. Optical microscopy (OM), scanning electron microscopy (SEM), and X-ray diffraction were used to characterize the microstructure of the coating. The wear resistance and corrosion resistance of the coating were evaluated by dry sliding wear and potentiodynamic polarization curve test methods, respectively. The results show that the coating was composed of a simple FCC solid solution phase with a microhardness about 3.7 times higher than that of the AZ91D matrix and even higher than that of the same high-entropy alloy prepared by an arc melting method. The coating had better wear resistance than the AZ91D matrix, and the wear rate was about 2.5 times lower than that of the AZ91D matrix. Moreover, the main wear mechanisms of the coating and the AZ91D matrix were different. The former was abrasive wear and the latter was adhesive wear. The corrosion resistance of the coating was also better than that of the AZ91D matrix because the corrosion potential of the former was more positive and the corrosion current was smaller.

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

Cutting fluid has been widely used to enhance the heat dissipation of cutting systems. However, whether cutting fluid can fully play its role is closely correlated with its thermophysical characteristics, such as viscosity, surface tension, etc. In this work, to study the effect of the thermophysical characteristics of cutting fluid on cutting performance, three green vegetable oils (semi-synthetic fluid (L1), rapeseed oil (L2), canola oil (L3)) were selected as cutting fluids of the MQL system, and differences in cutting performance were compared and analyzed under varied lubrication environments. Firstly, the thermophysical characteristics of the vegetable oils were determined by experimental methods. Afterwards, parameters, including tool wear, cutting force, and temperature, as well as the quality of machined workpieces, were selected to evaluate cutting performance, and essential reasons for the difference in cutting performance under varied lubrication environments were clarified. The results demonstrated that the cutting force, cutting temperature, and tool wear produced in the three MQL environments were lower than those in the dry cutting environment, while only the L1 and L2 MQL environments exhibited higher machined surface quality than the dry cutting environment. Moreover, obvious differences in cutting performance under the three MQL environments were also observed due to the different thermophysical characteristics of the three vegetable oils. The best cutting performance was achieved when L2 was used as the MQL cutting fluid. The efforts of this study will give an important reference for the choosing of green cutting fluid in the cutting process of difficult-to-cut materials and be of great significance for accelerating the development of green processing.

In the context of sustainable manufacturing practices, minimum quantity lubrication (MQL) has been extensively employed in machining operations involving hard-to-cut materials. While substantial experimental and numerical investigations on MQL-assisted machining have been conducted, existing simulation approaches remain inadequate for modeling the dynamic flow field variations inherent to MQL processes, significantly compromising the predictive reliability of current models. This study introduced an innovative bidirectional iterative coupling framework integrating finite element (FE) analysis and computational fluid dynamics (CFD) to enhance simulation accuracy. Since fluid flow characteristics critically influence tribological and thermal management at the tool–workpiece interface during machining, CFD simulations were initially performed to evaluate how MQL parameters govern fluid flow behavior. Subsequently, an integrated FE-CFD modeling approach was developed to simulate Ti-6Al-4V alloy turning under MQL conditions with varying feed rates. The novel methodology involved transferring thermal flux data from FE simulations to CFD’s heat source domain, followed by incorporating CFD-derived convective heat transfer coefficients back into FE computations. This repetitive feedback process continued until the thermal exchange parameters reached convergence. Validation experiments demonstrated that the proposed method achieved improved alignment between the simulated and experimental results for both cutting temperature profiles and principal force components across different feed conditions, confirming the enhanced predictive capability of this coupled simulation strategy.