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Generalized Neural Network Model to Predict the Properties of Sintered Al - Fe Composite

The deformation and strain hardening behaviour of Al-Fe composite preforms used in the metallurgical laboratory mainly depends on compacting load, aspect ratio, iron content, fractional density ratio and the die surface lubricant. Since these effects may not be linear and are usually interrelated, statistical methods are limited in their ability to predict the resulting process outcomes. Hence, the model was developed based on multi-layer Neural Network with a back propagation algorithm. Due to over-fitting, the conventional training method was not suitable to identify the required output parameters for unknown test data. Hence the standard tools like early stopping, regularization and Bayesian training were employed to enhance the neural network to recognize any independent test data.