Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices

This study investigates the process optimization and predictive modeling of femtosecond laser precision milling for commercial poly(methyl methacrylate) (PMMA) slices, with emphasis on surface roughness R_a and milling depth h. Three-dimensional surface morphology was measured using a laser confocal microscope, and the measurement methods for R_a and h were defined based on stable regions of interest and reference-plane correction. The effects of pulse energy, scanning line speed, scanning line spacing and pulse repetition frequency on milling quality were systematically analyzed. The results show that pulse energy and repetition frequency promoted material removal and increased milling depth, whereas scanning line speed and scanning line spacing reduced milling depth by decreasing the effective energy deposition per unit area. Surface roughness was influenced by both energy input and scanning uniformity, showing non-monotonic responses to scanning line speed and scanning line spacing. Quadratic response surface models were established using the Box-Behnken design. The ANOVA results indicate that both the R_a and h models were statistically significant, with R² values of 0.9970 and 0.9982, respectively. The validation results show that the average relative errors of the R_a and h models were 6.51% and 2.62%, respectively. These results demonstrate that the proposed models can effectively predict femtosecond laser milling quality and provide guidance for parameter selection and surface-quality control of commercial PMMA slices.