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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 Orcid Logo

International Journal of Hydrogen Energy

Swansea University Author: Sathiskumar Jothi Orcid Logo

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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...

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Published in: International Journal of Hydrogen Energy
ISSN: 0360-3199
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa35641
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first_indexed 2017-09-26T13:00:55Z
last_indexed 2018-02-09T05:27:03Z
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spelling 2018-01-30T09:56:12.3579986 v2 35641 2017-09-26 Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen 6cd28300413d3e63178f0bf7e2130569 0000-0001-7328-1112 Sathiskumar Jothi Sathiskumar Jothi true false 2017-09-26 EEN 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. Journal Article International Journal of Hydrogen Energy 0360-3199 Machine learning models; Hydrogen; Metallic materials; Mechanical properties 31 12 2017 2017-12-31 10.1016/j.ijhydene.2017.09.149 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-01-30T09:56:12.3579986 2017-09-26T08:55:34.9093659 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Titus Thankachan 1 K. Soorya Prakash 2 Christopher David Pleass 3 Devaraj Rammasamy 4 Balasubramanian Prabakaran 5 Sathiskumar Jothi 0000-0001-7328-1112 6 0035641-27092017090349.pdf thankachan2017.pdf 2017-09-27T09:03:49.6900000 Output 609056 application/pdf Accepted Manuscript true 2018-10-18T00:00:00.0000000 false eng
title Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
spellingShingle Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
Sathiskumar Jothi
title_short Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
title_full Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
title_fullStr Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
title_full_unstemmed Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
title_sort Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
author_id_str_mv 6cd28300413d3e63178f0bf7e2130569
author_id_fullname_str_mv 6cd28300413d3e63178f0bf7e2130569_***_Sathiskumar Jothi
author Sathiskumar Jothi
author2 Titus Thankachan
K. Soorya Prakash
Christopher David Pleass
Devaraj Rammasamy
Balasubramanian Prabakaran
Sathiskumar Jothi
format Journal article
container_title International Journal of Hydrogen Energy
publishDate 2017
institution Swansea University
issn 0360-3199
doi_str_mv 10.1016/j.ijhydene.2017.09.149
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description 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.
published_date 2017-12-31T03:44:24Z
_version_ 1763752085508063232
score 11.012924

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