Prediction of Extrusion Pressure And Product Deflection Of Using Artificial Neural Network

- In this paper artificial neural network was used as a modeling tool for simulation and prediction of extrusion pressure and product deflection of extrudes of lead alloys. An extensive experimental program was undertaken to extrude a lead (Pb) alloy on ELE Compact-1500 compression machine. The neural model of extrusion pressure and product deflection was developed based on groups of experiments carried out as samples, Eight (8) die bearing parameters (die bearing length, radius of curvature, slip angle, die angle, die ratio ram displacement, pocket depth and die diameter) were used as inputs into the network architecture of 8 [4-3]2 2 in predicting the extrusion pressure and product deflection. After series of network architectures were trained using different training algorithms such as Levenberg-Marquardt, Bayesian Regulation, Resilient Backpropagation using MATLAB 7.9.0 (R20096, the LM8 [4-3]2 2 was selected as the most appropriate model. Prediction of the neural model exhibited reasonable correlation with the experimental extrusion pressure and product deflection. The predicted extrusion pressure and product deflection gave reasonable errors and higher correlation coefficients indicating that the model is robust for predicting extrusion pressure and product deflection.