Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model

Abstract

The hot deformation behavior of AZ42 alloy was observed using thermal compression tests at a temperature scope of 250–400 °C and strain rate scope of 0.001–1 s−1. True stress-strain curves exhibited a combination of work hardening and dynamic softening features. A Northern Goshawk algorithm (NGO)-optimized artificial neural network (ANN) model was proposed. The established NGO-ANN model demonstrated impressive prediction accuracy, achieving a high determination coefficient of 0.991, a mean absolute percentage error of 3.51 %, and a root mean square error of 2.73. Subsequently, three-dimensional (3D) hot processing map based on the dynamic material model (DMM) theory was created. There were three different regions within the processing maps: the flow instability region (region A: 250–260 °C, 0.02–1 s−1, and region B: 300–400 °C, 0.01–0.1 s−1), high-power dissipation coefficient region (region C: 350–400 °C, 0.001–0.02 s−1, and region D: 300–350 °C, 0.5–1 s−1), and low power dissipation efficiency safety region (region E: the rest ones). Microstructural analysis revealed significant local plastic flow features in the flow instability region and a combination of coarse initial deformation grains and fine dynamic recrystallization (DRX) grains in the low power dissipation efficiency safety region. Fine and uniform grains were observed in the high-power dissipation efficiency region with DRX degree VDRX as high as 85.6 %, resulting in the best mechanical properties. Based on the established 3D hot processing map, the optimal process domains were determined.

Keywords

• AZ42 alloy
• Northern Goshawk optimization
• Artificial neural network model
• Processing map