### MATHEMATICAL MODELLING OF THE CO2 LASER CUTTING PROCESS USING GENETIC PROGRAMMING

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#### Abstract

_{2}laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots.

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Mukherjee, R., Goswami, D., Chakraborty, S., 2013, Parametric optimization of Nd: YAG laser beam machining process using artificial bee colony algorithm, Journal of Industrial Engineering, 2013, 570250.

Nassar, A., Nassar, E., Younis, M.A., 2016, Effect of laser cutting parameters on surface roughness of stainless steel 307, Leonardo Electronic Journal of Practices and Technologies, 29(2), pp. 127-136.

Singh, S.K., Gangwar, S., 2016, Parametric optimization of cutting parameters of laser assisted cutting using Taguchi analysis and genetic algorithm, i-Manager's Journal on Future Engineering and Technology, 11(3), pp. 36-42.

Gadallah, M.H., Abdu, H.M., 2015, Modeling and optimization of laser cutting operations, Manufacturing Review, 2(1), 20.

Abhimanyu, N., Satyanarayana, B., 2016, Optimization of CNC laser cutting process parameters, International Advanced Research Journal in Science, Engineering and Technology, 3(5), pp. 206-210.

Klancnik, S., Begic-Hajdarevic, D., Paulic, M., Ficko, M., Cekic, A., Husic, M.C., 2015, Prediction of laser cut quality for tungsten alloy using the neural network method, Journal of Mechanical Engineering, 61(12), pp. 714-720.

Bachy, B., Al-Dunainawi, Y., 2020, Influence of the effective parameters on the quality of laser micro-cutting process: Experimental analysis, modeling and optimization, Journal of Laser Applications, 32(1), 012002.

Chaki, S., Bose, D., Bathe, R.N., 2020, Multi-objective optimization of pulsed Nd: YAG laser cutting process using entropy-based ANN-PSO model, Lasers in Manufacturing and Materials Processing, 7(1), pp. 88-110.

Syn, C.Z., Mokhtar, M., Feng, C.J., Manurung, Y.H.P., 2011, Approach to prediction of laser cutting quality by employing fuzzy expert system, Expert Systems with Applications, 38(6), pp. 7558-7568.

Madić, M., Radovanović, M., Ćojbašić, Ž., Nedić, B., Gostimirović, M., 2015, Fuzzy logic approach for the prediction of dross formation in CO2 laser cutting of mild steel, Journal of Engineering Science and Technology Review, 8(3), pp. 143-150.

Rajamani, D., Tamilarasan, A., 2016, Fuzzy and regression modeling for Nd: YAG laser cutting of Ti-6Al-4V superalloy sheet, Journal for Manufacturing Science and Production, 16(3), pp. 153-162.

Zhang, Y.L., Lei, J.H., 2017, Prediction of laser cutting roughness in intelligent manufacturing mode based on ANFIS, Procedia Engineering, 174(1), pp. 82-89.

Pandey, A.K., Dubey, A.K., 2013, Fuzzy expert system for prediction of kerf qualities in pulsed laser cutting of titanium alloy sheet, Machining Science and Technology, 17(4), pp. 545-574.

Agarwal, S., Dandge, S.S., Chakraborty, S., 2020, Parametric analysis of a grinding process using the rough sets theory, Facta Universitatis-Series Mechanical Engineering, 18(1), pp. 91-106.

Ghadai, R.K., Kalita, K., Gao, X.Z., 2020, Symbolic regression metamodel based multi-response optimization of EDM process, FME Transactions, 48(2), pp. 404-410.

Mitra, A. P., Almal, A.A., George, B., Fry, D.W., Lenehan, P. F., Pagliarulo, V., Cote, R.J., Datar, R.H., Worzel, W.P., 2006, The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer, BMC cancer, 6(1), 159.

Chatterjee, S., Mahapatra, S.S., Bharadwaj, V., Upadhyay, B.N., Bindra, K.S., 2019, Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques, Engineering with Computers, 37(2), pp. 1181-1204.

Yunus, M., Alsoufi, M.S., 2019, Mathematical modeling of multiple quality characteristics of a laser microdrilling process used in Al7075/SiCp metal matrix composite using genetic programming, Modelling and Simulation in Engineering, 2019, 1024365.

Lestan, Z., Klancnik, S., Balic, J., Brezocnik, M., 2015, Modeling and design of experiments of laser cladding process by genetic programming and nondominated sorting, Materials and Manufacturing Processes, 30(4), pp. 458-463.

Hegab, H.A., Gadallah, M.H., Esawi, A.K., 2015, Modeling and optimization of electrical discharge machining (EDM) using statistical design, Manufacturing Review, 2, 21.

Gadallah, M.H., 2011, An alternative to Monte Carlo simulation method, International Journal of Experimental Design and Process Optimisation, 2(2), pp. 93-101.

Walker, M., 2001, Introduction to genetic programming, University of Montana, 2001.

Hrnjica, B., Danandeh Mehr, A., 2019, Optimized genetic programming applications: emerging research and opportunities, IGI Global, Hershey.

Koza, J.R., 1992, Genetic programming: On the programming of computers by means of natural selection, MIT Press.

Alvarez, L.F., 2000, Design Optimization Based on Genetic Programming, PhD thesis, University of Bradford, UK.

Hrnjica, B., 2016, Matematičko modeliranje inženjerskih problema korištenjem metode genetskog programiranja, 6st International Conference EDASOL 2016, Banja Luka, Bosnia and Herzegovina.

Alvarez, L.F., Toropov, V.V., Hughes, D.C., Ashour, A.F., 2000, Approximation model building using genetic programming methodology: applications, 2nd ISSMO/AIAA Internet Conference on Approximations and Fast Reanalysis in Engineering Optimization.

Genna, S., Menna, E., Rubino, G., Tagliaferri, V., 2020, Experimental investigation of industrial laser cutting: the effect of the material selection and the process parameters on the kerf quality, Applied Sciences, 10(14), 4956.

Tahir, A.F.M., Rahim, E.A., 2016, Study on the laser cutting quality of ultra high strength steel, Journal of Mechanical Engineering and Sciences, 10(2), pp. 2146-2159.

Adalarasan, R., Santhanakumar, M., Rajmohan, M., 2015, Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM), Measurement, 73, pp. 596-606.

Stelzer, S., Mahrle, A., Wetzig, A., Beyer, E., 2013, Experimental investigations on fusion cutting stainless steel with fiber and CO2 laser beams, Physics Procedia, 41(1), pp. 399-404.

Ozaki, H., Koike, Y., Kawakami, H., Suzuki, J., 2012, Cutting properties of austenitic stainless steel by using laser cutting process without assist gas, Advances in Optical Technologies, 2012, 234321.

Yilbas, B.S., Khan, S., Raza, K., Keles, O., Ubeyli, M., Demir, T., Karakas, M. S., 2010, Laser cutting of 7050 Al alloy reinforced with Al2O3 and B4C composites, International Journal of Advanced Manufacturing Technology, 50(1-4), pp. 185-193.

Madić, M., Radovanović, M., Gostimirović, M., 2015, ANN modeling of kerf taper angle in CO2 laser cutting and optimization of cutting parameters using Monte Carlo method, International Journal of Industrial Engineering Computations, 6(1), pp. 33-42.

EN ISO 9013:2002(E): Thermal cutting – Classification of thermal cuts – Geometrical product specification and quality tolerances, International Organization for Standardization, Geneva.

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