Elattar, Y., Mahdy, M., Sonbol, H. (2018). PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK. test, 18(18th International Conference on Applied Mechanics and Mechanical Engineering.), 1-14. doi: 10.21608/amme.2018.35013
Y. M. Elattar; M. A. Mahdy; H. A. Sonbol. "PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK". test, 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., 2018, 1-14. doi: 10.21608/amme.2018.35013
Elattar, Y., Mahdy, M., Sonbol, H. (2018). 'PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK', test, 18(18th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 1-14. doi: 10.21608/amme.2018.35013
Elattar, Y., Mahdy, M., Sonbol, H. PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK. test, 2018; 18(18th International Conference on Applied Mechanics and Mechanical Engineering.): 1-14. doi: 10.21608/amme.2018.35013

PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK

The International Conference on Applied Mechanics and Mechanical Engineering
Article 68, Volume 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., April 2018, Pages 1-14    PDF (1.44 M)
Document Type: Original Article
DOI: 10.21608/amme.2018.35013
Authors
Y. M. Elattar1; M. A. Mahdy2; H. A. Sonbol3
1Assistant Lecturer, Modern Academy for Engineering and Tech., Cairo, Egypt.
2Dean of Higher Institute for Engineering and Modern Technology Marg, Cairo, Egypt.
3Professor, Design and Prod. Eng. Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Abstract
ABSTRACT
This work presents a new predictive model of abrasive water-jet (AWJ) machining of
ARMOX shielding steel plate of 7.6 mm thick. The model was developed to predict
some interesting process parameters from process variables. As AWJ is a
complicated multi input multi output machining process. The model is developed
using artificial neural network (ANN). A feed forward neural network based on back
propagation was made up of 4 input neurons, 1 hidden layer with 10 hidden neurons
and 2 output neurons. The ANN training set was generated by extensive
experimental work. The tests considered four process variables. The studied AWJ
process variables are traverse speed (T), waterjet pressure (P), standoff distance (s),
and abrasive flow rate (ma). The considered process parameters are surface
roughness (Ra) and material removal rate (MRR). The ANN model was trained and
tested. The ANN succeeded to model the AWJ process by extracting the process
parameters from process variables with a regression factor above 90%. This paper is
a step forward to model and control the AWJ machining process.
Keywords
Abrasive water jet (AWJ); Armox; Artificial Neural network (ANN); surface roughness (Ra); Material Removal Rate (MRR)
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