Artificial Intelligence Model for the Prediction of Cut Quality in Abrasive Water Jet Cutting

Abstract:

In abrasive water jet cutting, the cut quality is of great importance. In this paper, artificial intelligence model was developed for the prediction of cut quality in abrasive water jet cutting of aluminum alloy. To this aim, artificial neural network (ANN) model was developed in terms of workpiece material thickness, traverse rate and abrasive flow rate. Three-layered feedforward ANN model having four hidden neurons trained with backpropagation algorithm with momentum was used for modeling purposes. The mathematical model showed high prediction accuracy with average absolute percentage error of about 3 %. Using the developed ANN model, 3-D graphs, showing the interaction effects of the traverse rate and abrasive flow rate for three different thicknesses, were given. It was showed that ANNs may be used as a good alternative in analyzing the effects of abrasive water jet cutting parameters on the cut quality characteristics.

• Periodical: Applied Mechanics and Materials (Volume 657)
• Pages: 206-210
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.657.206
• Online since: October 2014
• Authors: Miloš Madić*, Predrag Janković, Laurenţiu Slătineanu, Miroslav Radovanović
• Keywords: Abrasive Water Jet Cutting, Artificial Neural Network (ANN), Cut Quality, Modeling

References

[1] M. Madić, M. Radovanović, Mathematical modeling and analysis of AWJ cutting of carbon steel S275JR using ANN, Acad. J. Manuf. Eng. 9 (2011) 49-54.

[2] U. Caydaş, A. Hasçalik, A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method, J. Mater. Proces. Technol. 202 (2008) 574-582.

DOI: 10.1016/j.jmatprotec.2007.10.024

[3] D.S. Srinivasu, N. Ramesh Babu, A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle, Appl. Soft Comp. 8 (2008) 809-819.

DOI: 10.1016/j.asoc.2007.06.007

[4] J.J.R. Jegaraj, N. Ramesh Babu, A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear, J. Mater. Proces. Technol. 185(2007), 217-227.

DOI: 10.1016/j.jmatprotec.2006.03.124

[5] Y. Lu, X. Li, B. Jiao, Y. Liao, Application of artificial neural networks in abrasive waterjet cutting process, International Symposium on Neural Networks, Chongqing, China, 2005, 877-882.

DOI: 10.1007/11427469_139

[6] C. Ciupan, E. Ciupa, S. Ferenţ-Pipaş, Neural model for abrasive water jet cutting machine, Nonconv. Technol. Rev. 17 (2013) 25-29.

[7] C.X. Feng, Z.G. Yu, A. Kusiak, Selection and validation of predictive regression and neural network models based on designed experiments, IIE Trans. 38 (2006) 13-23.

DOI: 10.1080/07408170500346378