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Modelling on CVN toughness of weld deposits

The Charpy V Notch toughness (CVN) of steel is an important property while considering structural and heavy loading conditions. In welded structures, CVN is attributed to many variables like composition of steel, heat input of welding, pre- and post-heat treatments of the weldment, type of weldi...

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Main Authors: Chauhan, Rudrang, Nanavati, Purvesh, Pandit, Vinaykumar, Sharma, Shashank
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/20333/1/13.pdf
http://journalarticle.ukm.my/20333/
https://www.ukm.my/jkukm/volume-3404-2022/
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spelling my-ukm.journal.203332022-11-02T01:05:02Z http://journalarticle.ukm.my/20333/ Modelling on CVN toughness of weld deposits Chauhan, Rudrang Nanavati, Purvesh Pandit, Vinaykumar Sharma, Shashank The Charpy V Notch toughness (CVN) of steel is an important property while considering structural and heavy loading conditions. In welded structures, CVN is attributed to many variables like composition of steel, heat input of welding, pre- and post-heat treatments of the weldment, type of welding process etc. The regression analysis works accurately for three to four variables. The property of weldment is associated to more than three-four variables. So this conventional regression analysis couldn’t capture associated trends among the variables due to their non-linearity. This complexity is countered well by artificial neural network (ANN) modelling. In the present work, artificial neural network approach is utilized for the prediction of CVN of ferritic steel welds, which is multi-phase complex engineering material. The multilayer perceptron (MLP) method is used for formulating the neural network models. Numerous models were made by adjusting the hyperparameters and a best model was selected having least training error. The crucial results obtained from this model where analysed from response graphs and contour plot. This (MLP) approach for formulating neural network model was proved to be efficient after validation procedure and the same model could be exploited well for predicting accurate value of CVN in a very time and cost-effective manner. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20333/1/13.pdf Chauhan, Rudrang and Nanavati, Purvesh and Pandit, Vinaykumar and Sharma, Shashank (2022) Modelling on CVN toughness of weld deposits. Jurnal Kejuruteraan, 34 (4). pp. 649-657. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3404-2022/
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description The Charpy V Notch toughness (CVN) of steel is an important property while considering structural and heavy loading conditions. In welded structures, CVN is attributed to many variables like composition of steel, heat input of welding, pre- and post-heat treatments of the weldment, type of welding process etc. The regression analysis works accurately for three to four variables. The property of weldment is associated to more than three-four variables. So this conventional regression analysis couldn’t capture associated trends among the variables due to their non-linearity. This complexity is countered well by artificial neural network (ANN) modelling. In the present work, artificial neural network approach is utilized for the prediction of CVN of ferritic steel welds, which is multi-phase complex engineering material. The multilayer perceptron (MLP) method is used for formulating the neural network models. Numerous models were made by adjusting the hyperparameters and a best model was selected having least training error. The crucial results obtained from this model where analysed from response graphs and contour plot. This (MLP) approach for formulating neural network model was proved to be efficient after validation procedure and the same model could be exploited well for predicting accurate value of CVN in a very time and cost-effective manner.
format Article
author Chauhan, Rudrang
Nanavati, Purvesh
Pandit, Vinaykumar
Sharma, Shashank
spellingShingle Chauhan, Rudrang
Nanavati, Purvesh
Pandit, Vinaykumar
Sharma, Shashank
Modelling on CVN toughness of weld deposits
author_facet Chauhan, Rudrang
Nanavati, Purvesh
Pandit, Vinaykumar
Sharma, Shashank
author_sort Chauhan, Rudrang
title Modelling on CVN toughness of weld deposits
title_short Modelling on CVN toughness of weld deposits
title_full Modelling on CVN toughness of weld deposits
title_fullStr Modelling on CVN toughness of weld deposits
title_full_unstemmed Modelling on CVN toughness of weld deposits
title_sort modelling on cvn toughness of weld deposits
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2022
url http://journalarticle.ukm.my/20333/1/13.pdf
http://journalarticle.ukm.my/20333/
https://www.ukm.my/jkukm/volume-3404-2022/
_version_ 1748704125958225920
score 13.211869

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