Shape Memory Alloy (SMA) is a material that has the ability to memorize previous shapes after deforming. That is it regains its original shape when temperature increases, converting thermal energy to mechanical energy. This property of the plastic-like deformation which subsequently recovers its original shape is referred to as Shape Memory Effects (SMEs). The history of this material began in the year 1800s. Because of their unique behavior, SMA has great industrial applications. Many constituted models of SMA behavior are formed describing SMA behavior. Most of them are based on experimental phenomenological macroscopic constitutive models consisting of variables that reveal the degree of phase transition that describes the phenomenological macroscopic behavior of SMA. These types of models are very easy and the parameters are also very easy to determine. In this research, a constitutive model is formulated based on the obserbation of experimental data, the SMA behavior is simulated using Artificial Neural Networks (ANN). The phenomenological constitutive model comprises both mechanical and chemical change. In the parameter estimation, the Back-Progation (BP) algorithm and the nonlinear optimization algorithm are used. A numerical simulation is performed, and the phenomenological constitutive model captures well the uniaxial tension and compression experimental data, therefore the constitutive model is verified.
| Published in | American Journal of Mechanics and Applications (Volume 11, Issue 1) |
| DOI | 10.11648/j.ajma.20231101.12 |
| Page(s) | 6-14 |
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
NiTi Shape Memory Alloy, Phenomenological Model, Artificial Neural Network
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APA Style
Ahmad Abubakar, R. (2023). One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change. American Journal of Mechanics and Applications, 11(1), 6-14. https://doi.org/10.11648/j.ajma.20231101.12
ACS Style
Ahmad Abubakar, R. One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change. Am. J. Mech. Appl. 2023, 11(1), 6-14. doi: 10.11648/j.ajma.20231101.12
AMA Style
Ahmad Abubakar R. One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change. Am J Mech Appl. 2023;11(1):6-14. doi: 10.11648/j.ajma.20231101.12
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Download@article{10.11648/j.ajma.20231101.12,
author = {Rabiu Ahmad Abubakar},
title = {One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change},
journal = {American Journal of Mechanics and Applications},
volume = {11},
number = {1},
pages = {6-14},
doi = {10.11648/j.ajma.20231101.12},
url = {https://doi.org/10.11648/j.ajma.20231101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajma.20231101.12},
abstract = {Shape Memory Alloy (SMA) is a material that has the ability to memorize previous shapes after deforming. That is it regains its original shape when temperature increases, converting thermal energy to mechanical energy. This property of the plastic-like deformation which subsequently recovers its original shape is referred to as Shape Memory Effects (SMEs). The history of this material began in the year 1800s. Because of their unique behavior, SMA has great industrial applications. Many constituted models of SMA behavior are formed describing SMA behavior. Most of them are based on experimental phenomenological macroscopic constitutive models consisting of variables that reveal the degree of phase transition that describes the phenomenological macroscopic behavior of SMA. These types of models are very easy and the parameters are also very easy to determine. In this research, a constitutive model is formulated based on the obserbation of experimental data, the SMA behavior is simulated using Artificial Neural Networks (ANN). The phenomenological constitutive model comprises both mechanical and chemical change. In the parameter estimation, the Back-Progation (BP) algorithm and the nonlinear optimization algorithm are used. A numerical simulation is performed, and the phenomenological constitutive model captures well the uniaxial tension and compression experimental data, therefore the constitutive model is verified.
},
year = {2023}
}
TY - JOUR T1 - One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change AU - Rabiu Ahmad Abubakar Y1 - 2023/11/09 PY - 2023 N1 - https://doi.org/10.11648/j.ajma.20231101.12 DO - 10.11648/j.ajma.20231101.12 T2 - American Journal of Mechanics and Applications JF - American Journal of Mechanics and Applications JO - American Journal of Mechanics and Applications SP - 6 EP - 14 PB - Science Publishing Group SN - 2376-6131 UR - https://doi.org/10.11648/j.ajma.20231101.12 AB - Shape Memory Alloy (SMA) is a material that has the ability to memorize previous shapes after deforming. That is it regains its original shape when temperature increases, converting thermal energy to mechanical energy. This property of the plastic-like deformation which subsequently recovers its original shape is referred to as Shape Memory Effects (SMEs). The history of this material began in the year 1800s. Because of their unique behavior, SMA has great industrial applications. Many constituted models of SMA behavior are formed describing SMA behavior. Most of them are based on experimental phenomenological macroscopic constitutive models consisting of variables that reveal the degree of phase transition that describes the phenomenological macroscopic behavior of SMA. These types of models are very easy and the parameters are also very easy to determine. In this research, a constitutive model is formulated based on the obserbation of experimental data, the SMA behavior is simulated using Artificial Neural Networks (ANN). The phenomenological constitutive model comprises both mechanical and chemical change. In the parameter estimation, the Back-Progation (BP) algorithm and the nonlinear optimization algorithm are used. A numerical simulation is performed, and the phenomenological constitutive model captures well the uniaxial tension and compression experimental data, therefore the constitutive model is verified. VL - 11 IS - 1 ER -
Institute of Mechanical engineering Design, Mechanical Engineering Department, Zhejiang University, Hangzhou, China
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