Using Artificial Neural Networks to Investigate the Influence of Temperature on Hot Extrusion of AZ61 Magnesium Alloy

Abstract

The hot extrusion process of magnesium alloy involves many processing parameters, billet temperature is one of the parameters that directly affect the tensile strength of finished product. Hot extrusion experiments of involving rectangular tubes are conducted at selected billet temperatures of 320, 350, 380 and 400 °C. Artificial neural networks (ANN) analysis then is performed at increments of 10 °C each time between the temperature 320 and 400 °C. Consequently, the magnesium alloy product can be obtained at the optimum tensile strength, as well as the most suitable temperature range for billet heating during hot extrusion process. This study mainly explores the relationship between the billet temperature and product tensile strength of the hot extrusion of magnesium alloy, and obtains the optimum temperature range through ANN analysis, and analyzes the relationship between the temperature and the tensile strength of a rectangular tube for various extrusion speeds and extrusion ratios. Subsequently, experiments are performed to confirm the accuracy of the results by using ANN analysis at different extrusion speeds and extrusion ratios. Finally, observing the microstructure enables researchers to acquire the relationship between the sizes of the crystalline grain of the magnesium alloy product at the different formation temperature.