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.

In this investigation, spherical Al₂O₃ magnetic abrasive particles (MAPs) were used to polish the inner surface of ultra-fine long cobalt–chromium alloy cardiovascular stent tubes. The magnetic abrasives were prepared by combining plasma molten metal powder and hard abrasives, and the magnetic abrasives prepared by this new method are characterized by high sphericity, narrow particle size distribution range, long life, and good economic value. Firstly, the spherical Al₂O₃ magnetic abrasives were prepared by the new method; secondly, the polishing machine for the inner surface of the ultra-fine long cardiovascular stent tubes was developed; finally, the influence laws of spindle speed, magnetic pole speed, MAP filling quantities, the magnetic pole gap on the surface roughness (Ra), and the removal thickness (RT) of tubes were investigated. The results showed that the prepared Al₂O₃ magnetic abrasives were spherical in shape, and their superficial layer was tightly bound with Al₂O₃ hard abrasives with sharp cutting; the use of spherical Al₂O₃ magnetic abrasives could achieve the polishing of the inner surface of ultra-fine cobalt–chromium alloy cardiovascular bracket tubes, and after processing, the inner surface roughness (Ra) of the tubes decreased from 0.337 µm to 0.09 µm and had an RT of 5.106 µm.