Atomistic simulation of microstructure evolution of NiTi single crystals in bending deformation

Liu, Shan; Yuan, Lin; Han, Luyi; Wang, Xiebin; Zhao, Guoqun; Wang, Guangchun

doi:10.1016/j.commatsci.2021.110733

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…These processes, in turn, are intricately connected to performance-durability trade-offs in both functional 11 and structural 12 materials. Examples include temperature-dependent microstructure evolution 13 , 14 , hotspot formation 15 , 16 , and the nucleation and growth of new material phases 17 , 18 .…”

Section: Introductionmentioning

confidence: 99%

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

et al. 2023

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Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

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Section: Introductionmentioning

confidence: 99%

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

et al. 2023

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“…The energy dissipation mechanism changes from atomic interface friction in phase transformation to plastic deformation caused by grain boundary sliding. Shan Liu et al [21] studied the microstructure evolution of isoatomic NiTi shape memory alloy during bending deformation, respectively in the compression zone and the tensile zone. During bending deformation, linearly varying strain rates were applied in different regions, the microstructure evolution under compressive stress is different from that under tensile stress.…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics study on martensitic transformation behavior of SLM-NiTi alloy induced by temperature and stress

Che,

Shi,

et al. 2023

Phys. Scr.

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Selective laser melting (SLM) technology is currently one of the most promising additive manufacturing technologies for complex metal components. NiTi alloy has been highly regarded in advanced applications due to its excellent shape memory and good biocompatibility. However, as a new material, SLM-NiTi alloy is far from being applied in actual advanced fields. In the actual processing, such as grinding, turning, polishing, electrical discharge machining, all involve changes in temperature and stress, Therefore, it is very important to study the martensitic phase transition caused by temperature and stress changes in the precision machining process of SLM-NiTi alloy. However, it is difficult to observe the martensitic phase transition changes directly in the actual processing, so the method of molecular dynamics is adopted in this paper. Moreover, in the process of preparing NiTi alloy by selective laser melting, the ratio of Ni to Ti is very important, which determines the final forming quality. Therefore, this paper studied the martensitic transformation behavior induced by temperature and stress under different nickel proportions, different initial temperatures and different model sizes, and expounded the variation laws of stress-strain, potential energy, volume and dislocation. The microstructure and shear strain were demonstrated on the atomic scale. The results show that temperature plays an important role in the martensite transformation of SLM-NiTi alloy, low temperature will largely inhibit martensite transformation, and high temperature will promote martensite transformation. The stress induced martensite reorientation in SLM-NiTi alloy is accomplished by the migration of the interface between different martensite variants. When the nickel content is 52% and 55%, there is no inflection point between volume and potential energy with the change of temperature, when the nickel content is 50.8%, there is an obvious jump between volume and potential energy.

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“…These processes in turn are intricately connected to performance-durability trade-offs in both functional [8] and structural [9] materials. Examples include temperature-dependent microstructure evolution [10,11], hotspot formation [12,13], and the nucleation and growth of new material phases [14,15].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm

Chapman¹,

Hsu²,

Chen³

et al. 2022

Preprint

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Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this novel methodology to three prototypical examples with varying levels of disorder: (1) solidliquid interfaces, (2) polycrystalline microstructures, and (3) grain boundaries. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides an intuitive and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

show abstract

Atomistic simulation of microstructure evolution of NiTi single crystals in bending deformation

Cited by 9 publications

References 32 publications

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Molecular dynamics study on martensitic transformation behavior of SLM-NiTi alloy induced by temperature and stress

Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm

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