In this paper, the surface roughness of SiC ceramics was investigated in laser-assisted machining (LAM) processes; machine learning was used to predict surface roughness and to optimize the process parameters, and therefore, to ultimately improve the surface quality of a workpiece and obtain excellent serviceability. First, single-factor turning experiments were carried out on SiC ceramics using LAM according to the material removal mechanism to investigate the variation trend of the effects of different laser powers, rotational speeds, feed rates, and cutting depths on surface roughness. Then, laser power, rotational speed, feed rate and cutting depth were selected as the four factors, and the surface roughness was used as the target value for the orthogonal experiments. The results of the single-factor experiments and the orthogonal experiments were combined to construct a prediction model based on the combination of the grey wolf optimization (GWO) algorithm and support vector regression (SVR). The coefficient of determination (R²) of the optimized prediction model reached 0.98676 with an average relative error of less than 2.624%. Finally, the GWO algorithm was used to optimize the global parameters of the prediction model again, and the optimal combination of process parameters was determined and verified by experiments. The actual minimum surface roughness (Ra) value was 0.418 μm, and the relative error was less than 1.91% as compared with the predicted value of the model. Therefore, the prediction model based on GWO-SVR can achieve accurate prediction of the surface roughness of SiC ceramics in LAM and can obtain the optimum surface roughness using parameter optimization.

Despite extensive research over the past three decades proving that laser-assisted machining (LAM) is effective for machining ceramic materials, which are affected by many machining parameters, there has been no systematic study of the effects of process parameters on surface quality in LAM ceramic materials. In this paper, the effects and optimization of laser power, spindle speed, feed rate, and cutting depth on surface roughness and work hardening of LAM Si₃N₄ were systematically studied, using grey relational analysis coupled with the Taguchi method. The results show that the combination of machining parameters determines the material removal mode at the material removal location, and then affects the surface quality. In ductile material removal mode, both the value of surface roughness and work hardening degree are smaller. Decreased surface roughness and work hardening degree can be obtained with smaller cutting depth and higher laser power.

In this study, laser-assisted machining experiments are carried out on silicon carbide (SiC) ceramic materials by a turning process, and laser power, cutting depth, rotational speed, and feed speed are selected as research factors. In order to improve the surface processing quality of laser-assisted turning of SiC ceramics and obtain the smallest surface roughness, the orthogonal method and response surface method are used to investigate the effect of various factors on surface roughness. The effect of various factors on surface roughness is evaluated by variance analysis, mean analysis, main effect diagram, 3D response surface, and corresponding contour diagram. The surface roughness prediction model is established based on the response surface method, and the prediction error is 4.1% with high accuracy. The experimental results show that laser power and cutting depth are the most significant factors affecting surface roughness, and rotational speed is a significant factor affecting surface roughness. Under the optimum process conditions, the smallest surface roughness Ra obtained by the response surface method is 0.294 μm, which is lower than 0.315 μm obtained by the orthogonal method, and the surface quality is higher. Therefore, the optimal process parameters of the response surface method can obtain the smallest surface roughness and higher surface quality in laser-assisted turning of SiC ceramics.

A superhydrophobic surface with low adhesion and good wear resistance was fabricated on Ti6Al4V substrates via TiO₂/Ni composite electrodeposition, and subsequently modified with a fluoroalkylsilane (FAS) film. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and optical contact angle measurements were used to characterize the surface morphologies, chemical compositions, and surface wettability. The superhydrophobicity of the as-prepared surface results from the fabrication of a hierarchical structure and the assembly of low-surface energy fluorinated components. The as-prepared surface had a water contact angle as high as 162.6° and a sliding angle close to 1.8°. Scratch and abrasion tests showed that the superhydrophobic coating provided a superior wear resistance and stable mechanical abrasion protection. In addition, the influence of processing conditions, such as working voltage, deposited time, pH value, and TiO₂ concentration, was also investigated.

In this study, the water-jet-guided laser (WJGL) method was used to cut Inconel 718 alloy with high temperature resistance. The effect of critical parameters of the water-jet-guided laser machining method on the cutting depth was studied by a Taguchi orthogonal experiment. Furthermore, the mathematical prediction model of cutting depth was established by the response surface method (RSM). The validation experiments showed that the mathematical model had a high predictive ability for cutting depth. The optimal cutting depth was obtained by model prediction, and the error was 5.5% compared with the experimental results. Compared with the traditional dry laser cutting, the water conducting laser method reduced the thermal damage and improved the cutting quality. This study provides a reference for the precision machining of Inconel 718 with a water-jet-guided laser.