International Scientific Journal

External Links


This paper presents an investigation into the effect of the laser cutting parameters on the heat affected zone in CO2 laser cutting of AISI 304 stainless steel. The mathematical model for the heat affected zone was expressed as a function of the laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position using the artificial neural network. To obtain experimental database for the artificial neural network training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameter. Using the 27 experimental data sets, the artificial neural network was trained with gradient descent with momentum algorithm and the average absolute percentage error was 2.33%. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 6.46%. Statistically assessed as adequate, the artificial neural network model was then used to investigate the effect of the laser cutting parameters on the heat affected zone. To analyze the main and interaction effect of the laser cutting parameters on the heat affected zone, 2-D and 3-D plots were generated. The analysis revealed that the cutting speed had maximum influence on the heat affected zone followed by the laser power, focus position and assist gas pressure. Finally, using the Monte Carlo method the optimal laser cutting parameter values that minimize the heat affected zone were identified.
PAPER REVISED: 2012-07-04
PAPER ACCEPTED: 2012-07-12
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2012, VOLUME 16, ISSUE Supplement 2, PAGES [S363 - S373]
  1. Biswas, R., et al., Artificial Neural Network Modelling of Nd:YAG Laser Microdrilling on Titanium Nitride-Alumina Composite, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224 (2010), 3, pp. 473-482.
  2. Dubey, A. K., Yadava, V., Laser Beam Machining - A Review, International Journal of Machine Tools and Manufacture, 48, (2008), 6, pp. 609-628.
  3. Dutta Majumdar, J., Manna, I., Laser Processing of Materials, Sadhana, 28, (2003), 3-4, pp. 495-562.
  4. Meijer, J., Laser Beam Machining (LBM), State of the Art and New Opportunities, Journal of Materials Processing Technology, 149, (2004), 1-3, pp. 2-17.
  5. Radovanoviæ, M., Madiæ, M., Experimental Investigations of CO2 Laser Cut Quality: A Review, Nonconventional Technologies Review, 15, (2011), 4, pp. 35-42.
  6. Abdel Ghany, K., Newishy, M., Cutting of 1.2 mm Thick Austenitic Stainless Steel Sheet Using Pulsed and CW Nd:YAG Laser, Journal of Materials Processing Technology, 168, (2005), 3, pp. 438-447.
  7. Sheng, P. S., Joshi, V. S., Analysis of Heat-Affected Zone Formation for Laser Cutting of Stainless Steel, Journal of Materials Processing Technology, 53, (1995), 3-4, pp. 879-892.
  8. Dahotre, N. B., Harimkar, S. P., Laser Fabrication and Machining of Materials, Springer, Berlin, 2008.
  9. Mathew, G. L., et al., Parametric Studies on Pulsed Nd:YAG Laser Cutting of Carbon Fibre Reinforced Plastic Composites, Journal of Materials Processing Technology, 89-90, (1995), 3-4, pp. 198-203.
  10. Paulo Davim, J., et al., Some Experimental Studies on CO2 Laser Cutting Quality of Polymeric Materials, Journal of Materials Processing Technology, 198, (2008), 1-3, pp. 99-104.
  11. Rajaram, N., Sheikh-Ahmad, J., Cheraghi, S. H., CO2 Laser Cut Quality of 4130 Steel, International Journal of Machine Tools and Manufacture, 43, (2003), 4, pp. 351-358.
  12. Madiæ, M., Radovanoviæ, M., Comparative Modeling of CO2 Laser Cutting using Multiple Regression Analysis and Artificial Neural Network, International Journal of Physical Sciences, 7, (2012), 16, pp. 2422-2430.
  13. Fazeli, S. A., Rezvantalab, H., Kowsary, F., Thermodynamic Analysis and Simulation of a New Combined Power and Refrigeration Cycle using Artificial Neural Network, Thermal Science, 15, (2011), 1, pp. 29-31.
  14. Ganapathy, T., Gakkhar, R. P., Murugesan, K., Artificial Neural Network Modeling of Jatropha Oil Fueled Diesel Engine for Emission Predictions, Thermal Science, 13, (2009), 3, pp. 91-102.
  15. Hornik, K., Stinchcombe, M., White, H., Multilayer Feedforward Networks are Universal Approximators, Neural Networks, 2, (1989), 5, pp. 359-366.
  16. Cybenko, G., Approximation by Superpositions of a Sigmoidal Function, Mathematics of Control, Signals and Systems, 2, (1989), 4, pp. 303-314.
  17. Sumathi, S., Surekha, P., Computational Intelligence Paradigms: Theory and Applications Using MATLAB, CRC Press, Taylor & Francis Group., Boca Raton, 2010.
  18. Feng, C. X., Yu, Z. G., Kusiak, A., Selection and Validation of Predictive Regression and Neural Network Models Based on Designed Experiments, IIE Transactions, 38, (2006), 1, pp. 13-23.
  19. Sheikh-Ahmad, J. Y., Machining of Polymer Composites, Springer, Berlin, 2009.

© 2022 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence