Open AccessDetermination of the parameters of two-beam laser splitting of silicate glasses using regression and neural network models
Author(s) -
Y. V. Nikitjuk,
A. N. Serdyukov,
I.Y. Aushev
Publication year - 2022
Publication title -
žurnal belorusskogo gosudarstvennogo universiteta. fizika
Language(s) - English
Resource type - Journals
eISSN - 2617-3999
pISSN - 2520-2243
DOI - 10.33581/2520-2243-2022-1-35-43
Subject(s) - thermoelastic damping , artificial neural network , materials science , laser , beam (structure) , finite element method , workbench , regression analysis , optics , structural engineering , mechanical engineering , computer science , engineering , artificial intelligence , machine learning , physics , thermal , meteorology , visualization
The current work takes the results of the numerical experiment implemented in the Ansys finite element analysis program to create the neural network and regression models of two-beam laser splitting of silicate glasses. The regression models of two-beam laser glass cutting have been obtained in the DesignXplorer module of Ansys Workbench using a face-centered version of the central composite design. The processing speed, the parameters of laser beams, the glass plate thickness, and the distance between the laser radiation and the refrigerant affected zones were used as variable factors. The maximum temperatures and thermoelastic tensile stresses in the laser processing area were used as responses. The artificial neural networks have been constructed and trained using the TensorFlow package. The results of determining the maximum temperatures and thermoelastic stresses in the laser treatment area using the neural network and regression models have been compared.