• Emilia Ciupan Technical University of Cluj-Napoca
  • Cornel Ciupan Technical University of Cluj-Napoca
  • Florin Lungu Technical University of Cluj-Napoca
Keywords: waterjet, processing, neural, network, model


The paper presents a neural model used to control an abrasive water jet cutting machine and predict the cost of the process. The material features, the orifice diameter and the abrasive consumption are considered to be the input parameters. The output parameters are the feed rate, the energy consumption and the water consumption. A neural model with back propagation algorithm was used. A set of data obtained from the “Waterjet Web Reference Calculator” was used to model the process. The training and the validation data were calculated based on the values presented by the waterjet cutting machines manufacturers. In another paper [1] the authors have presented a neural model for controlling the speed of cutting and the abrasive consumption.


[1] Ciupan, C., Ciupan, E., Ferenţ-Pipaş, S. A., Neural model for abrasive water jet cutting machine, Nonconventional Technologies Review, Vol. XVII, No. 2, ISSN 1454-3087, pp. 25-29, (2013).
[2] Caydas, U., Hascalik, A., A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method, Journal of Materials Processing Technology, 202(1), pp. 574–582, (2008).
[3] Kolahan, F., Khajavi, H., Modeling and Optimization of Abrasive Waterjet Parameters using Regression Analysis, International Journal of Aerospace and Mechanical Engineering, pp. 248-253, (2011).
[4] Pop, A., Study of Computer Control Strategy for Jet Cutting Integrated Systems, Ph.D. Thesis, Technical University of Cluj-Napoca, (2004).
[5] Zain, A. M., Haron, H., Sharif, S., Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA, Expert Systems with Application, Vol. 38, Issue 7, pp. 8316–8326, (2011).
[6] Ciupan, C., Comşa, D., Ciupan, E., Simulating the thermoforming process of a box for upholstered furniture, Acta Technica Napocensis, Series Applied Mathematics, Mechanics, and Engineering, vol 61, Issue Special, September 2018, pp. 21-28.
How to Cite
Ciupan, E., Ciupan, C., & Lungu, F. (2018). THE COST ESTIMATION OF A WATER JET CUTTING PROCESS USING ARTIFICIAL NEURAL NETWORKS. Nonconventional Technologies Review, 22(4). Retrieved from