Journal article 716 views 367 downloads
Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
Titus Thankachan,
K. Soorya Prakash,
Christopher David Pleass,
Devaraj Rammasamy,
Balasubramanian Prabakaran,
Sathiskumar Jothi 
International Journal of Hydrogen Energy
Swansea University Author:
Sathiskumar Jothi
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DOI (Published version): 10.1016/j.ijhydene.2017.09.149
Abstract
Machine learning models were introduced to develop a relationship between the elemental composition and degraded mechanical properties in metallic materials due to the presence of hydrogen. Single layer and multilayer feed forward back propagation algorithm was developed as artificial neural network...
| Published in: | International Journal of Hydrogen Energy |
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| ISSN: | 0360-3199 |
| Published: |
2017
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa35641 |
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| Abstract: |
Machine learning models were introduced to develop a relationship between the elemental composition and degraded mechanical properties in metallic materials due to the presence of hydrogen. Single layer and multilayer feed forward back propagation algorithm was developed as artificial neural network based machine learning models to predict the mechanical properties of hydrogen charged metallic materials. Multilayer feed forward back propagation model was used to predicts the tensile strength, had a network topology of 12-13-3-2. And the single layer feed forward back propagation model was employed to predict the percentage of elongation, has a network topology of 12-11-1. The developed models were validated and tested with unknown inputs and their capability was studied. The models were evaluated using Mean Absolute (MAE) value and represented the scatter diagram to demonstrate the efficiency of the models. The R-value for both the models seems to prove that the models are ready to be used in the practical applications. |
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| Keywords: |
Machine learning models; Hydrogen; Metallic materials; Mechanical properties |
| College: |
Faculty of Science and Engineering |