Role of Grain Size and Oxide Dispersion Nanoparticles on the Hot Deformation Behavior of AA6063: Experimental and Artificial Neural Network Modeling Investigations

Abstract

The hot deformation behavior of coarse-grained (CG), ultrafine-grained (UFG), and oxide dispersion-strengthened (ODS) AA6063 is experimentally recognized though carrying out compression tests at different temperatures (300–450 °C) and strain rates (0.01–1 s−1). Microstructural studies conducted by TEM and EBSD indicate that dynamic softening mechanisms including dynamic recovery and dynamic recrystallization become operative in all the investigated materials depending on the regime of deformation. Moreover, the high temperature flow behavior is considerably influenced by the initial grain structure and the presence of reinforcement particles. The constitutive and artificial neural network (ANN) models were used to study the high-temperature flow behavior of the investigated alloys. To establish an accurate ANN model, material characteristics along with the processing parameters are deliberated. An Arrhenius type constitutive model with a strain-compensation term is employed to predict the flow stress of AA6063 alloys. The relative error associated with the constitutive and ANN models in the prediction of the flow stress is obtained 9.56% and 2.02%, respectively. The analysis indicates that the developed ANN model is more accurate in the prediction of flow stress with at least 78% less error in comparison to the constitutive model.