%0 Journal Article %T PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK %J The International Conference on Applied Mechanics and Mechanical Engineering %I Military Technical College %Z 2636-4352 %A Elattar, Y. M. %A Mahdy, M. A. %A Sonbol, H. A. %D 2018 %\ 04/01/2018 %V 18 %N 18th International Conference on Applied Mechanics and Mechanical Engineering. %P 1-14 %! PREDICTION OF ABRASIVE WATER JET CUTTING PARAMETERS USING ARTIFICIAL NEURAL NETWORK %K Abrasive water jet (AWJ) %K Armox %K Artificial Neural network (ANN) %K surface roughness (Ra) %K Material Removal Rate (MRR) %R 10.21608/amme.2018.35013 %X ABSTRACTThis work presents a new predictive model of abrasive water-jet (AWJ) machining ofARMOX shielding steel plate of 7.6 mm thick. The model was developed to predictsome interesting process parameters from process variables. As AWJ is acomplicated multi input multi output machining process. The model is developedusing artificial neural network (ANN). A feed forward neural network based on backpropagation was made up of 4 input neurons, 1 hidden layer with 10 hidden neuronsand 2 output neurons. The ANN training set was generated by extensiveexperimental work. The tests considered four process variables. The studied AWJprocess variables are traverse speed (T), waterjet pressure (P), standoff distance (s),and abrasive flow rate (ma). The considered process parameters are surfaceroughness (Ra) and material removal rate (MRR). The ANN model was trained andtested. The ANN succeeded to model the AWJ process by extracting the processparameters from process variables with a regression factor above 90%. This paper isa step forward to model and control the AWJ machining process. %U https://amme.journals.ekb.eg/article_35013_0ec333e5686002875d7557671df8b506.pdf