Hardness variation in cast-forging process of AZ80 magnesium alloys and its data-driven prediction

Abstract

This paper investigates the effects of the cast-forged process on the hardness distribution of the AZ80 magnesium alloy. AZ80 material is cast at four different cooling rates and die-forged at three forging temperatures. The microstructural characteristics of α-Mg grain size, Mg17Al12 phase dissolution and precipitation, and dynamic recrystallization are critical determinants of the material hardness. The processing parameters influence and provide controllability over these microstructural characteristics. Artificial neural network models are developed to predict the hardness based on a given combination of cooling rate, forging temperature, and location. This data-driven model is then used to predict the hardness values for all the measurement points on an unseen cooling rate and forging temperature, producing the predicted hardness contour maps. The model accurately captures the relationship attributed to the effect of processing parameters on the evolution of several different microstructure features. The prediction of the hardness distribution results in the percentage of the average hardness errors of 1.44 % ± 0.82 and 2.05 % ± 1.25 for one-I-beam-out and one-condition-out scenarios, respectively. The model’s average normalized root mean squared error values for one-I-beam-out and one-condition-out predictions are 0.0586 ± 0.014 and 0.0640 ± 0.016, respectively. The predicted contour maps accurately predict the hardness distribution of unseen cast-forged conditions and resemble the hardness variations in the actual cast-forged components.