Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion

This study establishes the database concerning magnesium alloy hot extrusion, and uses it to conduct various investigations. Firstly, artificial neural networks (ANN) analysis is used to determine the die shapes of various extrusion ratios. Secondly, the process parameters for the hot extrusion of magnesium alloy are determined, and thirdly, the tensile strength and maximum extrusion load of the finished product are predicted. The database includes 11 parameters, associated with 108 sets of experiment, determined by material type (AZ31 and AZ61), extrusion ratio (14.41, 35.9 and 55.85), product shape (tubular and sheet), semicone angle of the die (90 degrees and 30 degrees), extrusion speed, temperature to which the billet is heated, temperature to which the container is heated, lubricant, hold-time at a specified temperature, extrusion load and tensile strength. ANN is applied to learn from this database, and backward propagation analysis is conducted to find the mechanical properties of the products under various extrusion ratios. This study adopts the orthogonal array of the Taguchi method to hot extrusion experiments that involve dies with different extrusion ratios, and sets the tensile strength and extrusion load of the finished product as the quality characteristics, to acquire the optimal parameter condition. Then, based on the results obtained from the additive model, confirmatory experiments are performed. An Analysis of Variance (ANOVA) analysis is then performed to investigate and analyze the influence of factors on the hot extrusion process. The weight of important factors in the database is increased, and subsequently, the forming load and mechanical properties of magnesium alloy under extrusion are accurately predicted.