Optimisation of femtosecond laser micromachining of copper with AI algorithms

• Autorzy: Garasz Katarzyna , Kocik M. , Barbucha R. , Tański M. , Petrov T. S. , Mohamed-Seghir M.

Identyfikatory

DOI

10.5604/01.3001.0054.3220

• Języki publikacji: EN

Abstrakty

EN

To investigate the application of advanced artificial intelligence (AI) algorithms in optimising femtosecond laser micromachining processes for copper, addressing the critical need for precision and efficiency in micro-scale manufacturing. Design/methodology/approach The objectives are achieved by employing AI algorithms, including machine learning and neural networks, to analyse and optimise laser parameters for copper micromachining systematically. The approach combines theoretical modelling and experimental validation. Findings The research has shown that it is possible to apply artificial intelligence (AI) techniques for modelling and optimising quality characteristics in laser micromachining materials in a femtosecond regime. The results were promising with a challenging material and moderate-sized training set. The best validation performance was approx. 0.094. So far, this study provides basic guidelines for applying AI in laser micromachining of materials and adds to the newest research in this area. The results of the research also provide a deeper knowledge of the ablation mechanisms and will contribute to a coherent theory of ultrashort pulse laser–matter interactions. Research limitations/implications While this research represents a significant advancement in the field, it acknowledges certain limitations, primarily related to the complexity of femtosecond laser-material interactions and secondly related to small amounts of data, not easily scalable to feed the AI algorithms. Practical implications The outcomes of this study provide practical information for industries reliant on micro-scale manufacturing, such as electronics, micro-mechanics and precision instrumentation. Manufacturers can leverage the AI-driven optimization techniques presented here to improve product quality, reduce production costs, and expedite time-to-market. The optimised parameters result in reduced material waste, improved machining precision, and increased productivity. Originality/value The paper contributes to the field by demonstrating the original value of AI algorithms in optimizing femtosecond laser micromachining processes for copper. It introduces a novel approach marries cutting-edge AI techniques with intricate manufacturing processes, offering a unique perspective on the future of precision micro-manufacturing. The value of the research extends to materials scientists, manufacturing engineers, and AI practitioners seeking innovative solutions in materials processing and AI-driven optimisation.

Słowa kluczowe

EN

femtosecond laser micromachining; AI algorithms; copper; precision manufacturing; parameter optimization

PL

mikroobróbka laserowa; algorytmy AI; miedź; produkcja precyzyjna; optymalizacja parametrów

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2023
• Tom: Vol. 121, nr 2
• Strony: 267--274
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Garasz Katarzyna

• Institute of Fluid Flow Machinery, Polish Academy of Sciences, ul. Fiszera 14, 80-231 Gdańsk, Poland

• https://orcid.org/0000-0001-7073-717X

autor

Kocik M.

• Institute of Fluid Flow Machinery, Polish Academy of Sciences, ul. Fiszera 14, 80-231 Gdańsk, Poland

autor

Barbucha R.

• Institute of Fluid Flow Machinery, Polish Academy of Sciences, ul. Fiszera 14, 80-231 Gdańsk, Poland

autor

Tański M.

• Institute of Fluid Flow Machinery, Polish Academy of Sciences, ul. Fiszera 14, 80-231 Gdańsk, Poland

autor

Petrov T. S.

• Department of Applied Physics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, Bulgaria

autor

Mohamed-Seghir M.

• Faculty of Electrical Engineering, Gdynia Maritime University, ul. Morska 81/87, 81-225 Gdynia, Poland

Bibliografia

• [1] R.D. Schaeffer, Fundamentals of laser micromachining, CRC Press, Boca Raton, 2012, 1-30, 120-160.
• [2] G.M. Robinson, M.J. Jackson, Non-Conventional Processes: Laser Micromachining, in: J.P. Davim, M. Jackson (eds), Nano and Micromachining, ISTE, Londyn, 2009, 125-155. DOI: https://doi.org/10.1002/9780470611807.ch6
• [3] R. Ostrowski, J. Marczak, M. Sylwestrzak, E. Kwiatkowska, P. Targowski, Picosecond laser ablation system with process control by Optical Coherence Tomography, Proceedings of the SPIE: O3A: Optics for Arts, Architecture, and Archaeology II 7391 (2009) 73910G. DOI: https://doi.org/10.1117/12.827286
• [4] B.N. Chichkov, C. Momma, S. Nolte, F. van Alvensleben, A. Tunnerman, Femtosecond, picosecond and nanosecond laser ablation of solids, Applied Physics A 63 (1996) 109-115. DOI: https://doi.org/10.1007/BF01567637
• [5] J.M. Dowden (ed), The Theory of Laser Materials Processing: Heat and Mass Transfer in Modern Technology, Springer, Dordrecht, 2009. DOI: https://doi.org/10.1007/978-1-4020-9340-1
• [6] P.P. Pronko, S.K. Dutta, D. Du, Thermophysical effects in laser processing of materials with picosecond and femtosecond pulses, Journal of Applied Physics 78 (1995) 6233-6240. DOI: https://doi.org/10.1063/1.360570
• [7] X. Liu, D. Du, G. Mourou, Laser ablation and micromachining with ultrashort laser pulses, IEEE Journal of Quantum Electronics 33/10 (1997) 1706-1716. DOI: https://doi.org/10.1109/3.631270
• [8] B. Tan, A. Dalili, K. Venkatakrishnan, High repetition rate femtosecond laser nano-machining of thin films, Applied Physics A 95 (2009) 537-545. DOI: https://doi.org/10.1007/s00339-008-4938-8
• [9] Z. Chen, X. Fu, N. Geng, X. Hu, Effects of parameters in femtosecond laser micromachining on ablation of silicon, Transactions of Tianjin University 15 (2009) 225-228. DOI: https://doi.org/10.1007/s12209-009-0040-3
• [10] S. Preuss, A. Demchuk, M. Stuke, Sub-picosecond UV laser ablation of metals, Applied Physics A 61 (1995) 33-37. DOI: https://doi.org/10.1007/BF01538207
• [11] N.H. Rizvi, Femtosecond micromachining: Current status and applications, Riken Review 50, Focused on Laser Precision Microfabrication, 2003.
• [12] N.P. Mahalik, Micromanufacturing and nanotechnology, Springer Berlin, Heidelberg, 2006. DOI: https://doi.org/10.1007/3-540-29339-6
• [13] P.L. Liu, R. Yen, N. Bloemberger, R.T. Hodgson, Picosecond laser‐induced melting and resolidification morphology on Si, Applied Physics Letters 34 (1979) 864-866. DOI: https://doi.org/10.1063/1.90703
• [14] A. Žemaitis, M. Gaidys, M. Brikas, Rapid high-quality 3D micro-machining by optimised efficient ultrashort laser ablation, Optics and Lasers in Engineering 114(2019) 83-89. DOI: https://doi.org/10.1016/j.optlaseng.2018.11.001
• [15] K. Garasz, M. Tański, M. Kocik, E. Iordanova, G. Yankov, S. Karatodorov, M. Grozeva, The Effect of Process Parameters in Femtosecond Laser Micromachining, Bulgarian Journal of Physics 43/2 (2016) 110-120.
• [16] C. Kerse, H. Kalaycoğlu, P. Elahi, B. Çetin, D.K. Kesim, Ö. Akçaalan, S.Y.M.D. Aşk, B. Öktem, H. Hoogland, R. Holzwarth, F.Ö. Ilday, Ablation-cooled material removal with ultrafast bursts of pulses, Nature 537 (2016) 84-88. DOI: https://doi.org/10.1038/nature18619
• [17] A. Žemaitis, P. Gečys, M. Barkauskas, G. Račiukaitis, M. Gedvilas, Highly-efficient laser ablation of copper by bursts of ultrashort tuneable (fs-ps) pulses, Scientific Reports 9 (2019) 12280. DOI: https://doi.org/10.1038/s41598-019-48779-w
• [18] B. Rethfeld, D.S. Ivanov, M.E. Garcia, S.I. Anisimov, Modelling ultrafast laser ablation, Journal of Physics D: Applied Physics 50/19 (2017) 193001. DOI https://doi.org/10.1088/1361-6463/50/19/193001
• [19] S. Siano, R. Pini, R. Salimbeni, Variable energy blast modeling of the stress generation associated with laser ablation, Applied Physics Letters 74 (1999) 1233-1235. DOI: https://doi.org/10.1063/1.123509
• [20] A. Žemaitis, M. Gaidys, M. Brikas, P. Gečys, G. Račiukaitis, M. Gedvilas, Advanced laser scanning for highly-efficient ablation and ultrafast surface structuring: experiment and model, Scientific Reports 8 (2018) 17376. DOI: https://doi.org/10.1038/s41598-018-35604-z
• [21] A. Goeke, C. Emmelmann, Influence of laser cutting parameters on CFRP part quality, Physics Procedia 5/B (2010) 253-258. DOI: https://doi.org/10.1016/j.phpro.2010.08.051
• [22] A.K. Dubey, V. Yadava, Laser beam machining ‒ a review, International Journal of Machine Tools and Manufacture 48/6 (2008) 609-628. DOI: https://doi.org/10.1016/j.ijmachtools.2007.10.017
• [23] A.N. Bakhtiyari, Z. Wang, L. Wang, H. Zheng, A review on applications of artificial intelligence in modeling and optimization of laser beam machining, Optics and Laser Technology 135 (2021) 106721. DOI: https://doi.org/10.1016/j.optlastec.2020.106721
• [24] M. Madić, J. Antucheviciene, M. Radovanović, D. Petković, Determination of laser cutting proces conditions using the preference selection index method, Optics and Laser Technology 89 (2017) 214-220. DOI: https://doi.org/10.1016/j.optlastec.2016.10.005
• [25] M.G.M. Abdolrasol, S.M.S. Hussain, T.S. Ustun, M.R. Sarker, M.A. Hannan, R. Mohamed, J.A. Ali, S. Mekhilef, A. Milad, Artificial Neural Networks Based Optimization Techniques: A Review, Electronics 10/21 (2021) 2689. DOI: https://doi.org/10.3390/electronics10212689
• [26] E.I. Ageev, V.Y. Bychenkov, A.A. Ionin, S.I. Kudryashov, A.A. Petrov, A.A. Samokhvalov, V.P. Veiko, Double-pulse femtosecond laser peening of aluminum alloy AA5038: Effect of inter-pulse delay on transient optical plume emission and final surface micro-hardness, Applied Physics Letters 109 (2016) 211902. DOI: https://doi.org/10.1063/1.4968594
• [27] Y. Ren, J.K. Chen, Y. Zhang, Optical properties and thermal response of copper films induced by ultrashort-pulsed lasers, Journal of Applied Physics 110 (2011)113102. DOI: https://doi.org/10.1063/1.3662897
• [28] I. Baker, Copper, in: Fifty Materials That Make the World, Springer, Cham, 2018, 43-47. DOI: https://doi.org/10.1007/978-3-319-78766-4_9
• [29] Light Conversion. Available from: http://lightcon.com/Product/PHAROS.html (accessed at: 03.12.2022).
• [30] H. Okut, Bayesian, Regularized Neural Networks for Small n Big p Data, in: J.L.G. Rosa (ed), Artificial Neural Networks - Models and Applications, InTech, Rijeka, 2016. DOI: https://doi.org/10.5772/63256
• [31] M.H. Rahimi, M. Shayganmanesh, R. Noorossana, F. Pazhuheian, Modelling and optimization of laser engraving qualitative characteristics of Al-SiC composite using response surface methodology and artificial neural networks, Optics and Laser Technology 112 (2019) 65-76. DOI: https://doi.org/10.1016/j.optlastec.2018.10.058
• [32] J.K. Hoskins, H. Hu, M. Zou, Exploring Machine Learning and Machine Vision in Femtosecond Laser Machining, ASME Open Journal of Engineering 2 (2023) 024501. DOI: https://doi.org/10.1115/1.4063646
• [33] S.K. Dhara, A.S. Kuar, S. Mitra, An artificial neural network approach on parametric optimization of laser micro-machining of die-steel, International Journal of Advanced Manufacturing Technology 39 (2008) 39-46. DOI: https://doi.org/10.1007/s00170-007-1199-1