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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20333/ http://journalarticle.ukm.my/20333/1/13.pdf |
| _version_ | 1848815076187832320 |
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| author | Chauhan, Rudrang Nanavati, Purvesh Pandit, Vinaykumar Sharma, Shashank |
| author_facet | Chauhan, Rudrang Nanavati, Purvesh Pandit, Vinaykumar Sharma, Shashank |
| author_sort | Chauhan, Rudrang |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T00:44:13Z |
| format | Article |
| id | oai:generic.eprints.org:20333 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:44:13Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org: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/ |
| spellingShingle | Chauhan, Rudrang Nanavati, Purvesh Pandit, Vinaykumar Sharma, Shashank Modelling on CVN toughness of weld deposits |
| title | 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_short | Modelling on CVN toughness of weld deposits |
| title_sort | modelling on cvn toughness of weld deposits |
| url | http://journalarticle.ukm.my/20333/ http://journalarticle.ukm.my/20333/ http://journalarticle.ukm.my/20333/1/13.pdf |