Autors: K Garasz., M Kocik., R Barbucha., M Tański., Petrov, T. S., M Mohamed-Seghir.
Title: Optimisation of femtosecond laser micromachining of copper with AI algorithms
Keywords: fs laser micromachining, AI algorithms

Abstract: Purpose: 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 t

References

    Issue

    Journal of Achievements in Materials and Manufacturing Engineering, vol. 121, issue 2, pp. 267-274, 2023, Poland, International OCSCO World Press, DOI:10.5604/01.3001.0054.3220

    Цитирания (Citation/s):
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    2. Zheng, Nan, Ričardas Buividas, Hsin-Hui Huang, Dominyka Stonytė, Suresh Palanisamy, Tomas Katkus, Maciej Kretkowski, Paul R. Stoddart, and Saulius Juodkazis. "Laser machining at high∼ PW/cm2 intensity and high throughput." In Photonics, vol. 11, no. 7, p. 598. MDPI, 2024. - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus