Abstract
The paper investigated the efficacy of entropy-based ANN-PSO model combining Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) for estimation and optimization of quality characteristics associated with pulsed Nd:YAG laser cutting of aluminium alloy. In the ANN-PSO model, ANN trained using backpropagation with the Bayesian regularization algorithm is employed for estimation and computation of objective function value during optimization with PSO. The entropy method is used to compute the real weight of different output quality characteristics during formulation of the combined objective function. An experiment has been conducted based on full factorial experimental design, where cutting speed, pulse energy, and pulse width are considered as controllable input parameters while kerf width, kerf deviation, surface roughness, and material removal rate are measured as output parameters. Further, the experimental dataset has been used in the ANN-PSO model for prediction and optimization. The prediction accuracy of the ANN module is indicated by a low mean absolute error of 1.74%. Experimental validation of optimized output also results in less than 2% error only. ANOVA study suggests cutting speed as the most influencing factor.
Similar content being viewed by others
References
Steen, W.M., Mazumder, J.: Laser Material Processing, 4th edn. Springer, London/New York (2010)
Dubey, A.K., Yadava, V.: Optimization of kerf quality during pulsed laser cutting of aluminium alloy sheet. J Mater Process Tech. 204, 412–418 (2008)
Leone, C., Genna, S., Caggiano, A., Tagliaferri, V., Molitierno, R.: Influence of process parameters on kerf geometry and surface roughness in Nd:YAG laser cutting of Al 6061T6 alloy sheet. Int J Adv Manuf Tech. 87, 2745–2762 (2016)
Chaki, S., Ghosal, S., Bathe, R.N.: Kerf quality prediction and optimization for pulsed Nd:YAG laser cutting of aluminium alloy sheets using GA-ANN hybrid model. Int J Mechatron Manuf Syst. 5(3–4), 263–279 (2012)
Tamilarasan, A., Rajamani, D.: Multi-response optimization of Nd:YAG laser cutting parameters of Ti-6Al-4V superalloy sheet. J Mech Sci Technol. 31, 813–821 (2017)
Tamrin, K.F., Nukman, Y., Choudhury, I.A., Shirley, S.: Multiple-objective optimization in precision laser cutting of different thermoplastics. Opt Laser Eng. 67, 57–65 (2015)
Kuo, C.-F.J., Tsai, W.-L., Su, T.-L., Chen, J.-L.: Application of an LM-neural network for establishing a prediction system of quality characteristics for the LGP manufactured by CO2 laser. Opt Laser Technol. 43, 529–536 (2011)
Patel, P., Sheth, S., Patel, T.: Experimental analysis and ANN modelling of HAZ in laser cutting of glass fibre reinforced plastic composites. Procedia Technol. 23, 406–413 (2016)
Pandey, A.K., Dubey, A.K.: Modelling and optimisation of simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet. Opt Laser Technol. 44, 1858–1865 (2012)
Pardha Saradhi, V., Shashank, V., Saiteja, P., Anbarasu, G., Jagadesh, T.: Prediction of surface roughness and material removal rate in laser-assisted turning of aluminium oxide using fuzzy logic. Mater Today Proc. 5(9), 20343–20350 (2018)
Rajamani, D., Tamilarasan, A.: Fuzzy and regression modeling for Nd: YAG laser cutting of Ti-6Al-4V superalloy sheet. J Manuf Sci Prodn. 16(3), 2191–0375 (2016)
Shrivastava, P.K., Pandey, A.K.: Geometrical quality evaluation in laser cutting of Inconel-718 sheet by using Taguchi based regression analysis and particle swarm optimization. Infrared Phys Technol. 89, 369–380 (2018)
Pratihar, D.K.: Soft Computing: Fundamentals and Applications. Narosa Publishing House Pvt. Ltd., India (2015)
Deb, K.: Optimisation for Engineering Design: Algorithms and Examples, 8th edn. Prentice-Hall of India Private Limited, India (2005)
Tamilarasan, A., Rajamani, D.: Multi-objective optimization of Nd: YAG laser cutting parameters based on BBD-SA hybrid approach. J Engg Mat Sci. 24(4), 295–300 (2017)
Tamilasaran, A., Rajamani, D., Esakki, B.: Parametric optimisation in Nd-YAG laser cutting of thin Ti-6Al-4V superalloy sheet using evolutionary algorithms. Int J Mat Prod Technol. 57(1–3), 71–91 (2018)
Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE T Neural Netw. 5, 989–993 (1994)
Chaki, S., Ghosal, S.: Modelling and Optimisation of Laser Assisted Oxygen (LASOX) Cutting: a Soft Computing-Based Approach Springer Briefs in Computational Intelligence. Springer Verlag, Cham (2018)
Chaki, S., Bathe, R.N., Ghosal, S., Padmanabham, G.: Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN-NSGAII model. J Intel Manuf Sys. 29, 175–190 (2018)
Wen, K.L., Chang, T.C., You, M.L.: The grey entropy and its application in welding analysis. IEEE Sys Man Cybern. 2, 1842–1844 (1998)
Dubey, A.K., Yadava, V.: Multi-objective optimization of Nd:YAG laser cutting of nickel based superalloy sheet using orthogonal array with principal component analysis. Opt Laser Eng. 46, 124–132 (2008)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education Inc., Delhi/Chennai (2016)
Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Reference, Hershey (2010)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. Proc IEEE C Evol Computat. 1, 84–88 (2000)
Stournaras, A., Stavropoulos, P., Salonitis, K., Chryssolouris, G.: An investigation of quality in CO2 laser cutting of aluminum. CIRP J Manuf Sci Technol. 2(1), 61–69 (2009)
Sharma, V., Kumar, V.: Multi-objective optimization of laser curve cutting of aluminium metal matrix composites using desirability function approach. J Braz Soc Mech Sci Eng. 38, 1221–1238 (2016)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chaki, S., Bose, D. & Bathe, R.N. Multi-Objective Optimization of Pulsed Nd: YAG Laser Cutting Process Using Entropy-Based ANN-PSO Model. Lasers Manuf. Mater. Process. 7, 88–110 (2020). https://doi.org/10.1007/s40516-019-00109-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40516-019-00109-8