Enhanced Reversible Magnetic-Field-Induced Strain in Ni-Mn-Ga Alloy

A phase-field model was developed to simulate the ferromagnetic domain structure and martensite variant microstructure of Ni-Mn-Ga shape-memory alloy. The evolution of reversible magnetic-field-induced strain (MFIS) and associated magnetic domain/martensite variant structure were modeled under an external magnetic field. It was found that MFIS increased significantly from 0.2% to 0.28% as the temperature increased from 265 K to 285 K. In addition, compressive pre-stress efficiently enhanced the MFIS of the alloy, while tensile stress reduced MFIS. Furthermore, it was proved that there was possibility of achieving similar enhancement of MFIS by replacing compressive stress with perpendicular biaxial tensile stress. The results revealed that the residual variant induced by stress plays an important role in the reversible MFIS effect.

Quantitative analysis of compatible microstructure by electron backscatter diffraction

We propose a scheme for assigning the martensite variant using electron backscatter diffraction in a martensite material that undergoes a solid–solid phase transformation. Based on the solutions of the crystallographic equations of martensite, we provide an algorithm to assign martensite variants to a particular microscopic region, and to check the elastic compatibility of the microstructure corresponding to low hysteresis and high reversibility in shape memory alloys. This article is part of the theme issue ‘Topics in mathematical design of complex materials’.

Analytical modeling of the shape memory effect in SMA beams with rectangular cross section under reversed pure bending

An analytical model is developed for a prismatic SMA beam with rectangular cross section subjected to alternating bending at temperature below the austenitic transformations. The loading path consists in a loading-unloading cycle under bending and then under reversed bending. Two opposite martensitic variants take place, whose volume fractions evolve linearly with the axial stress. Different Young’s moduli are taken for the austenitic and martensitic phases. As the bending moment is increased, the martensitic transformation starts from the top and bottom and then it extends inwards. If the maximum applied bending moment is large enough, then the complete Martensitic transformation takes place at the upper and lower parts of the cross section. During unloading and the following reversed bending, reorientation of the Martensite variant into the opposite one takes place starting from the boundary between the fully martensitic region and the intermediate transforming region. Special attention is devoted to calculate analytically the axial stress and Martensite variant distributions within the cross section at each stage of the process. A closed form moment-curvature relation is provided for loading and elastic unloading and in integral form for the rest of the process. The approach is then validated by comparison with analytical results available in the literature.

Martensite Variant Identification For Shape Memory Alloys By Using Graph Neural Networks

Detailed microstructure evolution in shape memory alloys (SMAs) is typically studied by molecular dynamics (MD) simulations. However, the conventional post-processing tools for atomistic calculations, such as CNA and PTM, fail to identify distinct crystal variants and to reveal twin alignments in SMAs. In the current work, a powerful and efficient post-processing tool based on GraphSAGE neural network is developed, which can identify multiple phases in martensitic transformation, including the orthorhombic, monoclinic and R phases. Where the network was trained by the results of sets of temperatureand stress-induced martensitic transformation MD calculations. The accuracy and generality were also verified by the application to the cases which did not appear in the training dataset, such as unrecoverable nanoindentation process. The proposed method is rapid, accurate, and is ready to be integrated with any visualization tool for MD simulations. The outcome of the current work is expected to accelerate the pace of atomistic studies on SMAs and related materials.