Authors : Peeyush Kumar; Divya Chauhan

Volume/Issue : Volume 10 - 2025, Issue 12 - December

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DOI : https://doi.org/10.38124/ijisrt/25dec817

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Abstract : This study establishes robust empirical parameter–thickness relationship models essential for optimizing air- assisted mild steel cutting using a 1 kW fiber laser system. Efficient industrial application requires precise knowledge of how cutting parameters—particularly speed and focus position—must be adjusted to accommodate increasing material thickness while maintaining process stability and quality. The empirical phase involved determining the maximum cutting speed and corresponding optimal focus position for mild steel thicknesses ranging from 0.3 mm to 4.0 mm, all while maintaining a constant laser power of 100% (1 kW) and an assist gas pressure of 15 bar. Regression analysis by Artificial Intelligence revealed that the cutting speed exhibits an Exponential Decay relationship with thickness.

Keywords : Fibre Laser Cutting, Regression, Artificial Intelligence, Optimization, Thickness Relationship.

References :

1. A. Manuf, B. Process, and C. Systems, “An extensive review of the effects of laser cutting parameters on metal surface and kerf quality,” J. Mater. Sci., vol. 12, pp. 100–110, 2025.
2. D. E. Fiber, and F. Laser, “Experimental investigation of industrial laser cutting: The effect of the material selection and the process parameters on the kerf quality,” J. Manuf. Proc., vol. 8, pp. 201–215, 2020.
3. G. Control, and H. Parameter, “Empirical modeling and analysis of process parameters in laser beam cutting process,” IEEE J. Quantum Electron., vol. 15, pp. 320–335, 2024.
4. I. Automation, “Experimental analysis of laser cutting machine,” Proc. Conf. Adv. Manuf., vol. 6, pp. 401–412, 2021.
5. J. Quality, K. Surface, and L. Roughness, “A comprehensive review of studying the influence of laser cutting parameters on surface and kerf quality of metals,” J. Manuf. Technol., vol. 10, pp. 501–515, 2023.
6. M. Cutting, “An explanation of 'striation free' cutting of mild steel by fibre laser,” Opt. Laser Technol., vol. 42, pp. 601–610, 2010.
7. N. Algorithm, and O. Prediction, “Fuzzy MCDM methodology for analysis of fibre laser cutting process,” J. Mach. Learn. Appl., vol. 18, pp. 701–715, 2025.
8. P. Melt, Q. Ejection, and R. Thermal, “Improvement of laser beam fusion cutting of mild and stainless steel due to longitudinal, linear beam oscillation,” J. Heat Transfer, vol. 14, pp. 801–820, 2020.
9. S. Neural, and T. Network, “Modelling of fibre laser cutting via deep learning,” Artif. Intell. Eng., vol. 11, pp. 901–915, 2021.
10. U. Scoring, and V. Model, “Scoring model for fiber laser cutting of mild steel sheets,” Laser Eng., vol. 35, pp. 1001–1010, 2013.
11. W. Focus, and X. Position, “The importance of focal positions in laser cutting,” J. Opt. Eng., vol. 50, pp. 1101–1115, 2010.