Effect of addition of Ce and accumulative roll bonding on structure-property of the Mg-Ce-Al hybrid composite and its prediction and comparison using artificial neural network (ANN) approach

Abstract

Light alloys play a crucial role in realizing the national strategy for energy conservation and emission reduction, as well as promoting the upgrading of manufacturing industries. Mg/Al composite laminates combine the corrosion resistance and ductility of aluminium alloy with the lightweight characteristics of magnesium alloy. The addition of Ce (rare earth elements) can improve the mechanical properties of magnesium via grain refinement and improve the ductility of the hybrid composites. In the present work, an investigation on addition of Ce into the Mg/Al matrix through Accumulative Roll Bonding (ARB) has been presented. The Mg/Ce/Al hybrid composite consists of Mg-4%Zn alloy and Al 1100 alloy with 0.2% Ce particles added between the dissimilar layers. The changes occurred in the evaluation of microstructure, corrosion and mechanical properties of the Mg/Ce/Al hybrid composite as a result of deformation process and also the addition of Ce have been explicated. The ARB parameters: temperature, rolling speed, percentage reduction, and aging time, have been studied. An increase of about 2.36 times in strength and hardness of the hybrid composite, has been reported. Further, the structure–property relations in the Mg/Ce/Al hybrid composites were aslo predict and compare using machine learning models: Decision Tree and Multi-Layer Perceptron (MLP) models.

Keywords

• hybrid composite
• accumulative roll bonding
• grain refinement
• artificial neural network
• corrosion behavior