Surface Quality Control System of Laser Micromachining Based on Multi-Objective Optimization and Support Vector Regression

Aiming at the problem of large fluctuations in surface quality and difficulty in ensuring machining accuracy during laser micromachining, a laser micromachining surface quality control system based on multi-objective optimization and support vector regression (SVR) is proposed. The system aims to improve the uniformity and smoothness of the machined surface and reduce the probability of machining defects by reasonably adjusting the machining parameters. Firstly, by integrating the multi-objective optimization algorithm with the support vector regression model, combining experimental data with the real-time feedback mechanism, a closed-loop control system for dynamically adjusting the laser processing parameters is constructed. The multi-objective optimization algorithm module is used to optimize the key parameters in the laser micromachining process (such as laser power, scanning speed, and focal length), and a relationship model between processing parameters and surface quality is established. Next, the SVR algorithm module is used to train the surface quality prediction model based on a large amount of experimental data to achieve real-time prediction and evaluation of the processed surface quality. Finally, the optimization results and the prediction results of the SVR model are combined to perform closed-loop feedback control to dynamically adjust the machining parameters and further optimize the surface quality. When the laser power is 2W, the surface roughness is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.12 \mu \mathrm{m}$</tex>. As the power increases, the roughness drops to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.85 \mu \mathrm{m}$</tex> when the laser power reaches 10 W. When the laser power increases from 2W to 10W, the processing accuracy decreases from the initial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 5.2 \mu \mathrm{m}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 4.0 \mu \mathrm{m}$</tex>. The combination of multi-objective optimization and SVR model greatly improves the stability and predictability of surface quality and provides a new idea and method for quality control in the field of laser micromachining.