Face gears have complex tooth profiles, are difficult to manufacture, demand a high level of technical expertise, and traditional machining accuracy cannot meet production requirements. Therefore, femtosecond laser fine correction face gear was proposed as a new processing technology. In this study, we investigated the multifactor optimization of laser and correction parameters during the femtosecond laser ablation of face gear and presented theoretical guidance for the production field.
FemtoYL-100 all-fiber laser produced an 828 fs femtosecond laser to ablate 18Cr2Ni4WA, a three-dimensional digital video microscope observed the depth of ablation pits, and Hommel profilerHommel T8000 detected tooth surface roughness. First, a single-factor femtosecond laser right-angle S-type scanning was performed to machine the face gear material, and the influence degree and trend of laser correction parameters on the scanning track ablation depth and tooth surface roughness were obtained by combining the theoretical and experimental results. Based on the results, we analyzed and selected the value range of each factor and designed a factor-level table. According to the factor-level table, we performed multiple orthogonal experiments and tested the results of scanning track ablation depth and tooth surface roughness. The results were extremely poor. Furthermore, in the analysis of variance, after combining the two methods, the optimal laser parameters and combination of correction parameters were selected. A regression analysis model was established using the results of orthogonal experiments, and the power function was selected as the criterion model for modeling. After substituting the optimized parameter combination, the prediction results of the scanning track ablation depth and tooth surface roughness were obtained. A simulation model based on the ablation threshold and energy accumulation was established, and the predicted value of the scanning track ablation depth was obtained using MATLAB to solve the problem. Using optimized parameter combination to process the opposite gear material, measuring the scanning track ablation depth and tooth surface roughness, and comparing them with the regression and simulation models' results, the maximum relative error was within a reasonable range, verifying the feasibility of the orthogonal result.
According to single-factor experiments, the influence trends of laser power, repetition frequency, scanning speed, scanning track spacing, and defocusing distance on scanning track ablation depth and tooth surface roughness are obtained, and the parameter range is reduced. The results show that the energy density of the material increases and gradually reaches the ablation threshold as the laser power increases. Part of the material is vaporized, and the continuous decrease in temperature causes the material to melt into a liquid state, resulting in a pressure difference at the pit bottom, and the liquid material is difficult to discharge from the pit. The ablation depth and tooth surface roughness continue to increase (Fig. 6). As the repetition frequency increases, the number of times the pulsed laser acts on the material surface increases, the energy absorbed by the material increases, and the ablation depth and tooth surface roughness increase (Fig. 7). As the scanning speed increases, the spot overlap rate decreases, the contact between the femtosecond laser and material per unit area decreases, the energy obtained from the material decreases, and the ablation depth and tooth surface roughness decrease (Fig. 8). As the scanning track spacing increases, the lateral cumulative effect of the laser within a certain range intensifies, the ablation depth decreases, the scanning track spacing continues to increase, the energy accumulation degree is greater than the lateral accumulation, the ablation depth approaches linear growth, and the tooth surface roughness continues to increase (Fig. 9). An increase or decrease in the defocus will cause laser scattering, which will reduce the scanning track ablation depth, and the ablation will be incomplete, so when the defocus is 0, the tooth surface roughness is the smallest, and as the defocus amount increases or decreases, the tooth surface roughness will increase (Fig. 10). According to the single-factor results, we narrowed the range of parameters, designed orthogonal experiments (Tables 2 and 3), and used range and variance analyses to obtain optimal parameter combinations (Tables 4 and 5). We established a regression analysis model, used the least-squares method to calculate the orthogonal results, obtained the regression expressions (Tables 6 and 7), and substituted the optimal parameter combination to predict the value of scan channel ablation depth and tooth surface roughness. We also established a simulation model, calculated a material ablation threshold value of 0.1189 cm-2 according to the empirical formula, and used MATLAB to predict the value of scan channel ablation depth after substituting the optimal parameter combination. Experiments were performed according to the optimal parameter combination, and the experimental values of the scanning track ablation depth and tooth surface roughness were obtained. The maximum relative errors of ablation depth and tooth surface roughness from predicted value of regression model were 8.4% and 9.5%, whereas the maximum relative error of ablation depth from predicted value of simulation model was 5.7%, indicating the feasibility of the optimized results within a reasonable range.
The influence trend and degree of laser correction parameters on the ablation depth and tooth surface roughness of a scanning track are determined, and an orthogonal analysis table is established to obtain the optimal parameter combination. We establish a regression model, substitute the optimal parameters to predict the scan channel ablation depth and tooth surface roughness. We also established a simulation model, and use MATLAB to predict the experimental values of the scanning track ablation depth. The optimized parameter combination processing experiment is performed, and the maximum relative errors obtained after comparing the experimental value with predicted values of regression and simulation models are within a reasonable range, proving the rationality of the parameter optimization.
