Application of Artificial Neural Network (ANN) to Hot Extrusion of AZ61 Magnesium Alloy Structural Parts

Abstract

Many process parameters affect the mechanical properties of products of the extrusion of magnesium alloy. Among them, the heating temperature of billet and the extrusion speed have the greatest effect. Hence, in this study, the artificial neural network (ANN) method is applied to analyze the tensile strength of structural parts of the AZ61 magnesium alloy formed by hot extrusion. A few experimental data on the structural parts of hot-extruded magnesium alloy and acquired tensile strength data are added to the ANN database, established by our laboratory for learning, and the change in the tensile strength of the structural parts upon heating of the temperature of the billet to 300℃~340℃ with extrusion speeds of 1 mm/s~3 mm/s is analyzed. The study also proves the accuracy of the experimental ANN analytical results. Finally, a sectional hardness test is performed on the product to understand the hardness distribution of the extruded product. The mechanical properties of AZ61 magnesium alloy are compared with those of AZ31 magnesium alloy and A6061 aluminum alloy to understand the difference among these three materials.