Abstract
In this work, hot compress tests were conducted at the strain rate range of 1–0.001 s−1 and the temperature range of 900–1050 °C to unravel the hot deformation behavior of a powder metallurgy (PM) Al1.8CrCuFeNi2 high entropy alloy with BCC/B2 structure. Arrhenius model and radial basis function artificial neural network (RBF-ANN) machine learning (ML) method were used to model flow stress. RBF-ANN ML method possesses superior predictability. Elongated grains almost change to equiaxed grains under the low strain rate of 0.001 s−1 due to the activity of continuous dynamic recrystallization (CDRX). There are no significant changes in the distribution, size and content of fine B2 and BCC eutectic phases during deformation. Stress index ranges form 2.2–2.6 at various strains. Dislocation viscous gliding and grain-boundary sliding (GBS) are predominant deformation modes. The contribution of GBS to deformation is more significant at low strain rate. For dislocation slip, multiple slip systems are activated,{123̅}<111> slip system has higher Schmid factor. Hot processing ability is improved at high temperature and low strain rate. Absorbing of dislocation at grain boundaries and the dominance of GBS deformation mode under high temperature and low strain rate condition can avoid instability.
| Original language | English |
|---|---|
| Article number | 172828 |
| Journal | Journal of Alloys and Compounds |
| Volume | 972 |
| DOIs | |
| State | Published - 25 Jan 2024 |
| Externally published | Yes |
Keywords
- Flow stress prediction
- High entropy alloy
- Hot deformation behavior
- Microstructure
- Processing ability
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