Data-driven modelling of mechanical properties
Computational simulation of mechanical structures is an important tool for the design and optimization of structural components and the control of new processes. However, for simulations to be predictive, it is necessary to generate models that can accurately describe the mechanical properties o...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/159156 |
| _version_ | 1811688030358994944 |
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| author | Chang, Eldridge Wen Wei |
| author2 | Upadrasta Ramamurty |
| author_facet | Upadrasta Ramamurty Chang, Eldridge Wen Wei |
| author_sort | Chang, Eldridge Wen Wei |
| collection | NTU |
| description | Computational simulation of mechanical structures is an important tool for the design and optimization
of structural components and the control of new processes. However, for simulations to be predictive,
it is necessary to generate models that can accurately describe the mechanical properties of materials
under different states of stress. These constitutive laws are often determined on an ad-hoc basis, by
fitting the results of standard mechanical tests to phenomenological models.
In this Final Year Project, machine learning tools including Genetic Algorithm are utilized to develop
a data-driven model for materials behaviour. We compare these models with conventionally fitted ones
derived for analytical formulations. We test our strategy by fitting these models to experimental data
from a historical dataset for rubber. The fitting strategy of our model is to take data from one stress state
for training and validate our model by comparing its performance in two other stress states. |
| first_indexed | 2024-10-01T05:25:43Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/159156 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:25:43Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1591562023-03-04T20:11:27Z Data-driven modelling of mechanical properties Chang, Eldridge Wen Wei Upadrasta Ramamurty School of Mechanical and Aerospace Engineering A*STAR Institute of High Performance Computing Mark Hyunpong Jhon uram@ntu.edu.sg Engineering::Materials::Non-metallic materials Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Computational simulation of mechanical structures is an important tool for the design and optimization of structural components and the control of new processes. However, for simulations to be predictive, it is necessary to generate models that can accurately describe the mechanical properties of materials under different states of stress. These constitutive laws are often determined on an ad-hoc basis, by fitting the results of standard mechanical tests to phenomenological models. In this Final Year Project, machine learning tools including Genetic Algorithm are utilized to develop a data-driven model for materials behaviour. We compare these models with conventionally fitted ones derived for analytical formulations. We test our strategy by fitting these models to experimental data from a historical dataset for rubber. The fitting strategy of our model is to take data from one stress state for training and validate our model by comparing its performance in two other stress states. Bachelor of Engineering (Mechanical Engineering) 2022-06-10T12:26:42Z 2022-06-10T12:26:42Z 2022 Final Year Project (FYP) Chang, E. W. W. (2022). Data-driven modelling of mechanical properties. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159156 https://hdl.handle.net/10356/159156 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Materials::Non-metallic materials Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Chang, Eldridge Wen Wei Data-driven modelling of mechanical properties |
| title | Data-driven modelling of mechanical properties |
| title_full | Data-driven modelling of mechanical properties |
| title_fullStr | Data-driven modelling of mechanical properties |
| title_full_unstemmed | Data-driven modelling of mechanical properties |
| title_short | Data-driven modelling of mechanical properties |
| title_sort | data driven modelling of mechanical properties |
| topic | Engineering::Materials::Non-metallic materials Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/159156 |
| work_keys_str_mv | AT changeldridgewenwei datadrivenmodellingofmechanicalproperties |