Simulation and Prediction of the Vickers Hardness of AZ91 Magnesium Alloy Using Artificial Neural Network Model

Abstract

In this study, an artificial neural network (ANN) model was used to simulate and predict the Vickers hardness of AZ91 magnesium alloy. The samples of AZ91 alloy were aged at different temperatures (Ta = 100 to 300 °C) for different durations (ta = 4 to 192 h) followed by water quenching at 25 °C. The age-hardening response of the samples was investigated by hardness measurements. The microstructure investigations showed that only discontinuous precipitates formed at low aging temperatures (100 and 150 °C), while continuous precipitates invaded all the samples at a high aging temperature (300 °C). Both discontinuous and continuous precipitates formed at the intermediate aging temperatures (200 and 250 °C). X-ray diffraction (XRD) analysis revealed that the microstructure comprised two phases: The α-Mg matrix and intermetallic β-Mg17Al12 phase. The alteration of the crystalline lattice parameters a, c, and c/a ratio with the aging time at various aging temperatures was also investigated. Both c and c/a ratio had the same behavior with aging time while a had an inverse trend. The observed variations of the lattice parameters were attributed to the mode of precipitation in AZ91 alloy. The ANN findings for the simulation and prediction perfectly conformed to the experimental data.