Prediction of Abrasive Water Jet Cutting Parameters Using Artificial Neural Network

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 (m a ). The considered process parameters are surface roughness (R a ) 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. KEYWORDSAbrasive water jet (AWJ), Armox, artificial neural network (ANN), surface roughness (Ra), material removal rate (MRR) ---

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

“…The incoming signals' accuracy is improved with the use of weight factors W ij , which are networks of weight. The aggregate of the altered signals is then modified through the application of an exchange function that has a sigmoidal form [55][56][57]. The outcome of a regression ANN is a single node, denoted by the letter (y), which is determined by the activation function f and is derived from any node (j) that has been active in the past using the following Equation (2):…”