How to cite this paper
Madić, M., Radovanović, M & Gostimirović, M. (2015). Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method.International Journal of Industrial Engineering Computations , 6(1), 33-42.
Refrences
Baji?, D., Lela, B., & ?ivkovi?, D. (2008). Modeling of machined surface roughness and optimization of cutting parameters in face milling. Metalurgija, 47(4), 331-334.
Biswas, R., Kuar, A. S., Biswas, S. K., & Mitra, S. (2010). 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(3), 473-482.
Chaki, S., & Ghosal, S. (2011). Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel. Production Engineering, 5(3), 251-262.
Ciurana, J., Arias, G., & Ozel, T. (2009). Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Materials and Manufacturing Processes, 24(3), 358-368.
Dhara, S. K., Kuar, A. S., & Mitra, S. (2008). An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. The International Journal of Advanced Manufacturing Technology, 39(1-2), 39-46.
Dhupal, D., Doloi, B., & Bhattacharyya, B. (2007). Optimization of process parameters of Nd: YAG laser microgrooving of Al2TiO5 ceramic material by response surface methodology and artificial neural network algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(8), 1341-1350.
Dubey, A. K., & Yadava, V. (2008). Laser beam machining—a review. International Journal of Machine Tools and Manufacture, 48(6), 609-628.
Ghoreishi, M., & Nakhjavani, O. B. (2008). Optimisation of effective factors in geometrical specifications of laser percussion drilled holes. Journal of materials processing Technology, 196(1), 303-310.
Karnik, S. R., Gaitonde, V. N., Rubio, J. C., Correia, A. E., Abr?o, A. M., & Davim, J. P. (2008). Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model. Materials & Design, 29(9), 1768-1776.
Madi?, M., & Radovanovi?, M. (2013). Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(2), 103-110.
Madi?, M., Kova?evi?, M., & Radovanovi?, M. (2014). Application of multi-stage Monte Carlo method for solving machining optimization problems. International Journal of Industrial Engineering Computations, 5(4), 647-659.
Prajapati, B. D., Patel, R. J., & Khatri, B. C. (2013). Parametric investigation of CO2 laser cutting of mild steel and hardox-400 material. IJETAE, 3(4), 204-208.
Raja, T. V., Ruhil, A. P., & Gandhi, R. S. (2012). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications, 21(1), 119-124.
Rajaram, N., Sheikh-Ahmad, J., & Cheraghi, S. H. (2003). CO2 laser cut quality of 4130 steel. International Journal of Machine Tools and Manufacture, 43(4), 351-358.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24(6), 946-957.
Sha, W., & Edwards, K. L. (2007). The use of artificial neural networks in materials science based research. Materials & design, 28(6), 1747-1752.
Sivarao, S., Robert, M., & Samsudin, A. R. (2013). RSM Modelling and Optimization of CO2 Laser Machining of Industrial PVC Foam. International Review on Modelling and Simulations, 6(4), 1339-1343.
Syn, C. Z., Mokhtar, M., Feng, C. J., & Manurung, Y. H. (2011). Approach to prediction of laser cutting quality by employing fuzzy expert system. Expert Systems with applications, 38(6), 7558-7568.
Tsai, M. J., Li, C. H., & Chen, C. C. (2008). Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. Journal of Materials Processing Technology, 208(1), 270-283.
Yang, C. B., Deng, C. S., & Chiang, H. L. (2012). Combining the Taguchi method with artificial neural network to construct a prediction model of a CO2 laser cutting experiment. The International Journal of Advanced Manufacturing Technology, 59(9-12), 1103-1111.
Biswas, R., Kuar, A. S., Biswas, S. K., & Mitra, S. (2010). 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(3), 473-482.
Chaki, S., & Ghosal, S. (2011). Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel. Production Engineering, 5(3), 251-262.
Ciurana, J., Arias, G., & Ozel, T. (2009). Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Materials and Manufacturing Processes, 24(3), 358-368.
Dhara, S. K., Kuar, A. S., & Mitra, S. (2008). An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. The International Journal of Advanced Manufacturing Technology, 39(1-2), 39-46.
Dhupal, D., Doloi, B., & Bhattacharyya, B. (2007). Optimization of process parameters of Nd: YAG laser microgrooving of Al2TiO5 ceramic material by response surface methodology and artificial neural network algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(8), 1341-1350.
Dubey, A. K., & Yadava, V. (2008). Laser beam machining—a review. International Journal of Machine Tools and Manufacture, 48(6), 609-628.
Ghoreishi, M., & Nakhjavani, O. B. (2008). Optimisation of effective factors in geometrical specifications of laser percussion drilled holes. Journal of materials processing Technology, 196(1), 303-310.
Karnik, S. R., Gaitonde, V. N., Rubio, J. C., Correia, A. E., Abr?o, A. M., & Davim, J. P. (2008). Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model. Materials & Design, 29(9), 1768-1776.
Madi?, M., & Radovanovi?, M. (2013). Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(2), 103-110.
Madi?, M., Kova?evi?, M., & Radovanovi?, M. (2014). Application of multi-stage Monte Carlo method for solving machining optimization problems. International Journal of Industrial Engineering Computations, 5(4), 647-659.
Prajapati, B. D., Patel, R. J., & Khatri, B. C. (2013). Parametric investigation of CO2 laser cutting of mild steel and hardox-400 material. IJETAE, 3(4), 204-208.
Raja, T. V., Ruhil, A. P., & Gandhi, R. S. (2012). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications, 21(1), 119-124.
Rajaram, N., Sheikh-Ahmad, J., & Cheraghi, S. H. (2003). CO2 laser cut quality of 4130 steel. International Journal of Machine Tools and Manufacture, 43(4), 351-358.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24(6), 946-957.
Sha, W., & Edwards, K. L. (2007). The use of artificial neural networks in materials science based research. Materials & design, 28(6), 1747-1752.
Sivarao, S., Robert, M., & Samsudin, A. R. (2013). RSM Modelling and Optimization of CO2 Laser Machining of Industrial PVC Foam. International Review on Modelling and Simulations, 6(4), 1339-1343.
Syn, C. Z., Mokhtar, M., Feng, C. J., & Manurung, Y. H. (2011). Approach to prediction of laser cutting quality by employing fuzzy expert system. Expert Systems with applications, 38(6), 7558-7568.
Tsai, M. J., Li, C. H., & Chen, C. C. (2008). Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. Journal of Materials Processing Technology, 208(1), 270-283.
Yang, C. B., Deng, C. S., & Chiang, H. L. (2012). Combining the Taguchi method with artificial neural network to construct a prediction model of a CO2 laser cutting experiment. The International Journal of Advanced Manufacturing Technology, 59(9-12), 1103-1111.