In laser drilling, precise parameter optimization is essential to achieving the desired hole characteristics. This study investigates the influence of the pulse width and pulse spacing on the machined hole geometry and proposes an artificial intelligence-based framework to predict hole shapes in multilayer composite substrates. The distribution of hole diameters resulting from CO₂ laser machining was evaluated via response surface methodology, considering variations in the pulse width and irradiation time. The results demonstrated a strong dependency of the hole diameters on the laser conditions and revealed significant autocorrelation among the machined-hole parameters.

1. [1] M. Mizoshiri et al., “Effect of different solvents on Cu micropatterns formed via femtosecond laser reduction patterning,” Int. J. Automation Technol., Vol.10, No.6, pp. 934-940, 2016. https://doi.org/10.20965/ijat.2016.p0934
2. [2] Z. Lin et al., “Realization of ∼10 nm features on semiconductor surfaces via femtosecond laser direct patterning in far field and in ambient air,” Nano Letters, Vol.20, No.7, pp. 4947-4952, 2020. https://doi.org/10.1021/acs.nanolett.0c01013
3. [3] R. A. Rahman et al., “First step toward laser micromachining realization by photonic nanojet in water medium,” Int. J. Automation Technol., Vol.15, No.4, pp. 492-502, 2021. https://doi.org/10.20965/ijat.2021.p0492
4. [4] S. Papazoglou and I. Zergioti, “Laser induced forward transfer (LIFT) of nano-micro patterns for sensor applications,” Microelectronic Engineering, Vol.182, pp. 25-34, 2017. https://doi.org/10.1016/j.mee.2017.08.003
5. [5] H. Shen et al., “Interferometric laser imaging for droplet sizing revisited: Elaboration of transfer matrix models for the description of complete systems,” Applied Optics, Vol.51, No.22, pp. 5357-5368, 2012. https://doi.org/10.1364/AO.51.005357
6. [6] M. R. Emmert-Buck et al., “Laser capture microdissection,” Science, Vol.274, No.5289, pp. 998-1001, 1996. https://doi.org/10.1126/science.274.5289.998
7. [7] A. Mahrle and E. Beyer, “Theoretical aspects of fibre laser cutting,” J. of Physics D: Applied Physics, Vol.42, No.17, Article No.175507, 2009. https://doi.org/10.1088/0022-3727/42/17/175507
8. [8] M. B. Prime et al., “Laser surface-contouring and spline data-smoothing for residual stress measurement,” Experimental Mechanics, Vol.44, pp. 176-184, 2004. https://doi.org/10.1007/BF02428177
9. [9] P. Milgrom and J. Roberts, “The economics of modern manufacturing: Technology, strategy, and organization,” The American Economic Review, Vol.80, No.3, pp. 511-528, 1990.
10. [10] T. Nakayama et al., “The quality evaluation by laser drilling process and in-process monitoring of the printed circuit board,” Laser Thermal Processing Study Group Papers, Vol.48, pp. 145-152, 1999 (in Japanese).
11. [11] T. Hirogaki et al., “Laser drilling of blind via holes in aramid and glass/epoxy composites for multi-layer printed wiring boards,” Composites Part A: Applied Science and Manufacturing, Vol.32, No.7, pp. 963-968, 2001. https://doi.org/10.1016/S1359-835X(00)00152-4
12. [12] P. Rajesh et al., “Experimental and parametric studies of Nd:YAG laser drilling on austenitic stainless steel,” The Int. J. of Advanced Manufacturing Technology, Vol.93, pp. 65-71, 2017. https://doi.org/10.1007/s00170-015-7639-4
13. [13] M. A. Bezerra et al., “Response Surface Methodology (RSM) as a tool for optimization in analytical chemistry,” Talanta, Vol.76, No.5, pp. 965-977, 2008. https://doi.org/10.1016/j.talanta.2008.05.019
14. [14] J. A. Ghani, I. A. Choudhury, and H. H. Hassan, “Application of Taguchi method in the optimization of end milling parameters,” J. of Materials Processing Technology, Vol.145, No.1, pp. 84-92, 2004. https://doi.org/10.1016/S0924-0136(03)00865-3
15. [15] A. N. Bakhtiyari et al., “A review on applications of artificial intelligence in modeling and optimization of laser beam machining,” Optics & Laser Technology, Vol.135, Article No.106721, 2021. https://doi.org/10.1016/j.optlastec.2020.106721
16. [16] A. Alizadeh and H. Omrani, “An integrated multi response Taguchi-neural network-robust data envelopment analysis model for CO₂ laser cutting,” Measurement, Vol.131, pp. 69-78, 2019. https://doi.org/10.1016/j.measurement.2018.08.054
17. [17] J. Jacob, P. Shanmugavelu, and R. Balasubramaniam, “Investigation of the performance of 248 nm excimer laser assisted photoresist removal process in gaseous media by response surface methodology and artificial neural network,” J. of Manufacturing Processes, Vol.38, pp. 516-529, 2019. https://doi.org/10.1016/j.jmapro.2019.01.002
18. [18] Y. Wang et al., “Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection,” Applied Soft Computing, Vol.77, pp. 188-204, 2019. https://doi.org/10.1016/j.asoc.2019.01.015
19. [19] G. Ke et al., “LightGM: A highly efficient gradient boosting decision tree,” Advances in Neural Information Processing Systems, Vol.30, pp. 3149-3157, 2017. https://dl.acm.org/doi/10.5555/3294996.3295074
20. [20] Y. Wang et al., “Modeling and simulation for a drop-impact analysis of multi-layered printed circuit boards,” Microelectronics Reliability, Vol.46, Nos.2-4, pp. 558-573, 2006. https://doi.org/10.1016/j.microrel.2005.05.007
21. [21] L. Breiman, “Bagging predictors,” Machine Learning, Vol.24, pp. 123-140, 1996. https://doi.org/10.1007/BF00058655