Optimization of Quartz Sol-gel Glass Cutting Parameters by Elliptical Laser Beams Using Neural Network Simulation and Genetic Algorithm

This study provides the optimization of double-beam cutting of quartz plates through laser cleaving. Neural network simulation and the authors’ version of the modified genetic algorithm were used to determine the optimal processing parameters. Finite element calculations of temperature and thermoelastic stress fields were performed to create the training data array and the array data for testing neural networks. Neural networks and their training algorithms were implemented with the Keras library in Python. The optimal neural network architectures for approximating the maximum values of tensile stresses and temperature during laser cutting of quartz plates were determined. The genetic algorithm was used to find the optimal parameter values for quarts plate laser cutting process.