Determination and optimization of the parameters of the technological operation of two-beam laser cutting of silicate glass using a genetic algorithm and neural networks

Abstract

This work investigates the numerical modeling and optimization of dual-beam laser cleaving for silicate glasses. Thermoelastic stresses and temperature fields were computed via quasi-static finite element analysis using FEniCS. A modified genetic algorithm optimized laser power, speed, and spot radius to maximize tensile stress and processing speed. Neural network approximations yielded errors below 4% for temperature and 5% for stress. A real-time adaptive control method based on neuroregulators was developed to maintain processing precision and stability.