Annular drilling process and quality control neural network model of stainless steel micro-hole with femtosecond laser

Theoretical and experimental research on annular drilling of injector micro-hole by using the femtosecond laser micromachining system. The 06Cr19Ni10 stainless steel was used as the target material, and the orthogonal experiments with 5 factor and 5 level were designed based on L₂₅(5⁵) orthogonal table. The significance level of the influence of laser power, repetition frequency, defocus, scanning speed and scanning times on micro-hole processing was analyzed and the formation and evolution rule of micro-hole were explored. Then, the influence of various parameters on the micro-hole accuracy and topography was explored. Finally, the relatively optimal laser processing parameters were as follows: laser power was 1.0 W, repetition frequency was 9.0 kHz, defocus distance was 200 μm, scanning speed was 1.0 mm/s, scanning times was 40 times. In addition, based on BP neural network, a mapping model with the above five parameters as input and micro-hole entrance and exit aperture as output was set up. The results show that the prediction error of the relationship model is less than 7.6% by iterative training and verification of orthogonal experimental data.