Parameters optimization of silicate glass two-beam asymmetric laser splitting

Nikitjuk, Y.V., Sereda, A.A., Serdyukov, A.N., Shalupaev, S.V., Aushev, I.Y.

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Никитюк Ю.В., Середа А.А., Сердюков А.Н., Шалупаев С.В., Аушев И.Ю. Оптимизация параметров двухлучевого ассиметричного лазерного раскалывания силикатного стекла // Оптический журнал. 2023. Т. 90. № 6. С. 15–24. http://doi.org/10.17586/1023-5086-2023-90-06-15-24

Nikityuk Yu.V., Sereda A.A., Serdyukov A.N., Shalupaev S.V., Aushev I.Yu. Parameters optimization of silicate glass two-beam asymmetric laser splitting [In Russian] // Opticheskii Zhurnal. 2023. V. 90. № 6. P. 15–24. http://doi.org/10.17586/1023-5086-2023-90-06-15-24

For citation (Journal of Optical Technology):

Yuri Nikitjuk, Andrey Sereda, Anatoly Serdyukov, Sergey Shalupaev, and Igor Aushev, "Parametric optimization of silicate-glass-based asymmetric two-beam laser splitting," Journal of Optical Technology. 90(6), 296-301 (2023)

Abstract:

Subject of study. Optimization of parameters of nonthrough oblique cracks in the process of twobeam laser asymmetric cracking of silicate glasses based on metamodeling. The purpose of the work. Development of models for the selection of optimal technological modes for applying inclined cracks during laser splitting of silicate glass with specified parameters, increasing the efficiency of the process of creating rounded edges during twobeam asymmetric laser splitting of glass products. Method. Multicriteria search for optimal technological regimes of laser processing using the MOGA genetic algorithm for the formation of rounded edges with specified geometric parameters in glass products. Main results. The calculation of the geometric parameters of inclined cracks, temperatures and thermoelastic stresses was performed using the APDL programming language of the ANSYS program. For the numerical experiment, a facecentered version of the central compositional plan of the experiment was used. The experimental plan was formed for four factors (P1–P4): the laser radiation power of the YAG laser, the radiation wavelength of which is 1.06 µm; laser radiation power of a CO₂ laser, the radiation wavelength of which is 10.6 µm; shifting the center of the YAG laser beam relative to the center of the CO₂ laser beam in a direction perpendicular to the material processing line; the speed of movement of laser beams and coolant along the material processing line. The maximum values of temperature and thermoelastic stresses in the laser treatment zone, as well as the geometric parameters of the inclined crack induced by laser radiation, were used as responses. The influence of technological regimes of material processing (factors P1–P4) on the geometrical parameters of an inclined crack has been evaluated. The most significant technological parameters of glass products processing, which affect the maximum temperatures that occur in the material during laser thermal splitting, are the processing speed and the power of laser radiation with a wavelength of 10.6 µm. Significant parameters that affect the magnitude of thermoelastic stresses are the processing speed and the power of laser radiation with a wavelength of 1.06 µm. In this case, the geometric characteristics of inclined cracks depend significantly on the radiation power of the YAG laser and on the displacement of the center of the beam of this laser relative to the center of the CO₂ laser beam. Using the TensorFlow package, an artificial neural network was created, the training and testing of which was carried out on samples formed when solving the corresponding problems by the finite element method in the ANSYS program. The results of determining the output parameters using a neural network model and a model created using the nonparametric regression method are compared. The following criteria were used to optimize asymmetric glass cracking: maximum tensile stresses and maximum processing speed for given values of the oblique crack parameters. The use of the genetic algorithm ensured the maximum relative error of the results, not exceeding 9% when determining temperatures and 12% when determining thermoelastic stresses. The relative error in determining the values of the geometric parameters of inclined cracks did not exceed 20%. Practical significance. The proposed technique makes it possible to determine the technological regimes for the formation of laserinduced nonthrough oblique cracks with specified geometric parameters.

Keywords:

laser splitting, glass plate, optimization, MOGA, ANSYS

OCIS codes: 350.3390

References:

1. Lumley R.M. Controlled separation of brittle materials using a laser // Am. Ceram. Soc. Bull. 1969. V. 48. P. 850–854.
2. Machulka G.A. Laser processing of glass. M.: Sov. radio, 1979. 136 p.
3. Kondratenko V.S. Method of splitting non-metallic materials // Pat. US № 5609284. 1997.
4. Nisar S., Li L., Sheikh M. Laser glass cutting techniques — a review // Journal of Laser Applications. 2013. V. 25. № 4. P. 042010-1–042010-11. https://doi.org/10.2351/1.4807895
5. Kondratenko V.S., Naumov A.S. The method of blunting the sharp edges of products // Pat. RF No. 2426700. 2009.
6. Nikityuk Yu.V., Serdyukov A.N., Aushev I.Yu. Optimization of two-beam laser cleavage of silicate glass // Journal of Optical Technology. 2022. V. 89. № 2. P. 121–125. https://doi.org/10.1364/JOT.89.000121
7. Junke J., Xinbing W. Cutting glass substrates with dual-laser beams // Optics and Lasers in Engineering. 2009. V. 47. Iss. 7–9. P. 860–864. https://doi.org/
10.1016/j.optlaseng.2008.12.009
8. Sysoev V.K., Vyatlev P.A., Chirkov A.V. et al. Concept of two-laser thermal splitting of glass elements for space vehicles // Bulletin “FSUE NPO im. S.A. Lavochkin». 2011. № 1. P. 38–44.
9. Zhao C., Zhang H., Yang L., Wang Y., Ding Y. Dual laser beam revising the separation path technology of laser induced thermal-crack propagation for asymmetric linear cutting glass // International Journal of Machine Tools and Manufacture. 2016. V. 106. P. 43–55. https://doi.org/10.1016/j.ijmachtools.2016.04.005.
10. Serdyukov A.N., Shalupaev S.V., Nikityuk Yu.V., Sereda A.A. Simulation of the process of two-beam asymmetric thermal splitting of brittle non-metallic
materials // Izvestiya of the Francisk Skoryna Gomel State University. 2011. № 6 (69). P. 124–127.
11. Agalakov Yu.G., Bernstein A.V. Data dimension reduction in simulation modeling problems // Information Technologies and Computing Systems. 2012. № 3. P. 3–17.
12. Koziel S., Leifsson L. Surrogate-based modeling and optimization. Applications in engineering. New York: Springer, 2013. 412 р. https://doi.org/10.1007/978-1-4614-7551-4
13. Jiang P., Zhou Q., Shao X. Surrogate model-based engineering design and optimization. Singapore: Springer, 2020. 240 р. https://doi.org/10.1007/978-981-15-0731-1
14. Kadri M.B., Nisar S., Khan S.Z., Khan W.A. Comparison of ANN and finite element model for the prediction of thermal stresses in diode laser cutting of float glass // Optik — International Journal for Light and Electron Optics. 2015. V. 126. № 19. P. 1959–1964. http://doi.org/10.1016%2Fj.ijleo.2015.05.033
15. Nikitjuk Y.V., Serdyukov A.N., Aushev I.Y. Determination of the parameters of two-beam laser splitting of silicate glasses using regression and neural network models // Journal of the Belarusian State University. Physics. 2022. V. 1 P. 35–43. https://doi.org/10.33581/2520-2243-2022-1-35-43
16. Golovko V.A., Krasnoproshin V.V. Neural network technologies for data processing: Proc. allowance. Minsk: BGU, 2017. 263 p.
17. Chollet F. Deep Learning with Python. N.Y.: Manning, 2018. 400 p.
18. Bessmeltsev V.P., Bulushev E.D. Optimization of laser micromachining modes // Avtometriya. 2014. V. 50. № 6. P. 3–21.
19. Parandoush P., Hossain A. A review of modeling and simulation of laser beam machining // International Journal of Machine Tools and Manufacture. 2014. V. 85. P. 135–145. https://doi.org/10.1016/j.ijmachtools.2014.05.008
20. Emelyanov V.V., Kureichik V.V., Kureichik V.M. Theory and practice of evolutionary modeling. M.: FIZMATLIT, 2003. 432 p.
21. Krasnovskaya S.V., Naprasnikov V.V. A review of the possibilities of optimization algorithms in modeling the structures of compressor-condensing units by the finite element method // Vesti National Academy of Sciences of Belarus. Physics and technical sciences series. 2016. № 2. P. 92–98.
22. Fonsecay C., Flemingz P. Genetic algorithms for multiobjective optimization: Formulation discussion and generalization // In Proceedings of The 5th International Conference on Genetic Algorithms. CA, USA. San Francisco: Morgan Kaufmann Publishers Inc., 1993. P. 416–423.
23. ANSYS [Electronic resource] / Official site of the ANSYS company. Access mode: https://www.ansys.com/ (accessed 11/21/2022)

24. Morgunov A.P., Revina I.V. Planning and analysis of experimental results. Omsk: Publishing House of OmGTU, 2014. 343 p.
25. Santner T.J., Williams B.J., Notz W.I. The design and analysis of computer experiments. NY: Springer New York, 2003. 285 p. https://doi.org/10.1007/978-1-4757-3799-8.