Improvement of shape recovery by controlling the microstructure and mechanical properties in a thermomechanically-treated Fe-based alloy
Source abstract
Abstract
In this research, the effects of thermomechanical treatment including heavy cold rolling and post-deformation annealing on the improvement of shape recovery in an Fe–9Ni–7Mn (at.%) martensitic alloy were investigated. Experimental results showed that the 80 % cold rolling produced a little fraction of strain-induced austenite phase in the martensitic matrix due to the strain concentration in the microstructure and the temperature rise under deformation. During post-deformation annealing at 590 °C in a dual phase (α+γ) region, a greater fraction of the austenite was introduced in the martensitic matrix such that its amount first increased by increasing the annealing time to 1.8 ks and then decreased at a more time of reversion. The microstructural evolution revealed that the austenite reversion under post-deformation annealing initially occurred by a shear nature at the early steps of reversion and then continued by diffusion. Tensile testing represented a good mixture of a relatively high ultimate tensile strength of ∼780 MPa and proper ductility of ∼8.5 % in the post-deformation annealed (PDA) sample for 0.9 ks. In addition, the shape memory properties were examined under the conditions of cold rolling and post-deformation annealing. The results showed a maximum shape recovery rate of ∼23 % for the cold rolled specimen which increased to ∼50 % after post-deformation annealing for 0.9 ks. The presence of a relatively high amount of retained austenite with proper stability and strength in the microstructure of the PDA sample for 0.9 ks was the reason for achieving the highest value of shape recovery.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Shape Memory Alloy Transformations · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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