On intrinsic time measure in the modeling of cyclic behavior of a Nitinol cubic block

Chiroiu, Veturia; Ionescu, Marius Florinel; Sireteanu, Tudor; Ioan, Rodica; Munteanu, Ligia

doi:10.1088/0964-1726/24/3/035022

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Residual martensite reduces the grain size rearranging the microstructure and hinders the motion of small grains. This ultimately results in a stronger NiTi material [ 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Structural Porosity and Martensite Evolution on Mechanical Characteristics of Nitinol via In-Silico Finite Element Approach

et al. 2022

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Nitinol (NiTi) alloys are gaining extensive attention due to their excellent mechanical, superelasticity, and biocompatibility properties. It is difficult to model the complex mechanical behavior of NiTi alloys due to the solid-state diffusionless phase transformations, and the differing elasticity and plasticity presenting from these two phases. In this work, an Auricchio finite element (FE) model was used to model the mechanical behavior of superelastic NiTi and was validated with experimental data from literature. A Representative Volume Element (RVE) was used to simulate the NiTi microstructure, and a microscale study was performed to understand how the evolution of martensite phase from austenite affects the response of the material upon loading. Laser Powder Bed Fusion (L-PBF) is an effective way to build complex NiTi components. Porosity being one of the major defects in Laser Powder Bed Fusion (L-PBF) processes, the model was used to correlate the macroscale effect of porosity (1.4–83.4%) with structural stiffness, dissipated energy during phase transformations, and damping properties. The results collectively summarize the effectiveness of the Auricchio model and show that this model can aid engineers to plan NiTi processing and operational parameters, for example for heat pump, medical implant, actuator, and shock absorption applications.

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“…Residual martensite reduces the grain size rearranging the microstructure and hinders the motion of small grains. This ultimately results in a stronger NiTi material [ 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Structural Porosity and Martensite Evolution on Mechanical Characteristics of Nitinol via In-Silico Finite Element Approach

et al. 2022

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On intrinsic time measure concept with application to the modeling of smart materials

Ioan

2016

Proc Appl Math and Mech

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In this paper, the cyclic behavior of a Nitinol cubic block is described by using the Bouc‐Wen model [2] coupled to the intrinsic time measure, other than the clock time which governs the behavior of the materials [4]. The modified Bouc‐Wen model consists from the classical Bouc‐Wen model coupled to the intrinsic time measure, other than the clock time which governs the behaviour of the materials. As a consequence, the thermodynamic admissibility of the Bouc–Wen model is provided by the endochronic theory of plasticity. The role of the intrinsic time measure is described by capturing the stiffness and strength degradation and the opposite phenomena. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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Steady-state and transient mechanical response analysis of superelastic nitinol lattice structures prior to additive manufacturing: An in-silico study

Khan

Ayub

Chekotu

et al. 2023

Journal of Materials Research and Technology

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On intrinsic time measure in the modeling of cyclic behavior of a Nitinol cubic block

Cited by 3 publications

References 23 publications

Influence of Structural Porosity and Martensite Evolution on Mechanical Characteristics of Nitinol via In-Silico Finite Element Approach

Influence of Structural Porosity and Martensite Evolution on Mechanical Characteristics of Nitinol via In-Silico Finite Element Approach

On intrinsic time measure concept with application to the modeling of smart materials

Steady-state and transient mechanical response analysis of superelastic nitinol lattice structures prior to additive manufacturing: An in-silico study

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