Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić, M.; Radovanović, M.; Gostimirović, M.

Growing Science » International Journal of Industrial Engineering Computations » Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Journals

IJIEC Volumes

Volume 1 (17)
- Issue 1 (9)
- Issue 2 (8)

Volume 2 (68)
- Issue 1 (12)
- Issue 2 (20)
- Issue 3 (20)
- Issue 4 (16)

Volume 3 (76)
- Issue 1 (9)
- Issue 2 (15)
- Issue 3 (20)
- Issue 4 (12)
- Issue 5 (20)

Volume 4 (50)
- Issue 1 (14)
- Issue 2 (10)
- Issue 3 (12)
- Issue 4 (14)

Volume 5 (47)
- Issue 1 (13)
- Issue 2 (12)
- Issue 3 (12)
- Issue 4 (10)

Volume 6 (39)
- Issue 1 (7)
- Issue 2 (12)
- Issue 3 (10)
- Issue 4 (10)

Volume 7 (47)
- Issue 1 (10)
- Issue 2 (14)
- Issue 3 (10)
- Issue 4 (13)

Volume 8 (30)
- Issue 1 (9)
- Issue 2 (7)
- Issue 3 (8)
- Issue 4 (6)

Volume 9 (32)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (7)
- Issue 4 (10)

Volume 10 (34)
- Issue 1 (8)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (8)

Volume 11 (36)
- Issue 1 (9)
- Issue 2 (8)
- Issue 3 (9)
- Issue 4 (10)

Volume 12 (29)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 13 (41)
- Issue 1 (10)
- Issue 2 (8)
- Issue 3 (10)
- Issue 4 (13)

Volume 14 (50)
- Issue 1 (11)
- Issue 2 (15)
- Issue 3 (9)
- Issue 4 (15)

Volume 15 (55)
- Issue 1 (19)
- Issue 2 (15)
- Issue 3 (12)
- Issue 4 (9)

Volume 16 (12)
- Issue 1 (12)

Countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 6 Issue 1 pp. 33-42 , 2015

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method Pages 33-42 Download PDF

Authors: Miloš Madić, Miroslav Radovanović, Marin Gostimirović

DOI: 10.5267/j.ijiec.2014.9.003

Keywords: Artificial neural network, CO2 laser cutting, Kerf taper, Modeling, Monte Carlo method, Optimization

Abstract: In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

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.