Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel
X80 pipeline steel is widely used in oil and gas pipelines because of its excellent strength, toughness, and corrosion resistance. It is welded via gas metal arc welding (GMAW), risking high cold crack sensitivities. There is a certain relationship between the joint hardness and cold crack sensitivi...
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MDPI AG
2024-12-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/15/1/14 |
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| author | Chen Yan Haonan Li Die Yang Yanan Gao Jun Deng Zhihang Zhang Zhibo Dong |
| author_facet | Chen Yan Haonan Li Die Yang Yanan Gao Jun Deng Zhihang Zhang Zhibo Dong |
| author_sort | Chen Yan |
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| description | X80 pipeline steel is widely used in oil and gas pipelines because of its excellent strength, toughness, and corrosion resistance. It is welded via gas metal arc welding (GMAW), risking high cold crack sensitivities. There is a certain relationship between the joint hardness and cold crack sensitivity of welded joints; thus, predicting the joint hardness is necessary. Considering the inefficiency of welding experiments and the complexity of welding parameters, we designed a set of processes from temperature field analysis to microstructure prediction and finally hardness prediction. Firstly, we calculated the thermal cycle curve during welding through multi-layer welding numerical simulation using the finite element method (FEM). Afterwards, BP neural networks were used to predict the cooling rates in the temperature interval that ferrite nuclears and grows. Introducing the cooling rates to the Leblond function, the ferrite fraction of the joint was given. Based on the predicted ferrite fraction, mapping relationships between joint hardness and the joint ferrite fraction were built using BP neural networks. The results shows that the error during phase fraction prediction is less than 8%, and during joint hardness prediction, it is less than 5%. |
| format | Article |
| id | doaj-art-8b3fea9fb31f4b68876e74bf8bd82a4e |
| institution | Kabale University |
| issn | 2073-4352 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj-art-8b3fea9fb31f4b68876e74bf8bd82a4e2025-01-24T13:28:01ZengMDPI AGCrystals2073-43522024-12-011511410.3390/cryst15010014Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline SteelChen Yan0Haonan Li1Die Yang2Yanan Gao3Jun Deng4Zhihang Zhang5Zhibo Dong6China Petroleum Pipeline Research Institute Co., Ltd., Langfang 065000, ChinaNational Key Laboratory of Precision Welding and Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, ChinaChina Petroleum Pipeline Research Institute Co., Ltd., Langfang 065000, ChinaPipeChina Engineering Technology Innovation Co., Ltd., Tianjin 300450, ChinaChina Petroleum Pipeline Research Institute Co., Ltd., Langfang 065000, ChinaNational Key Laboratory of Precision Welding and Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Precision Welding and Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, ChinaX80 pipeline steel is widely used in oil and gas pipelines because of its excellent strength, toughness, and corrosion resistance. It is welded via gas metal arc welding (GMAW), risking high cold crack sensitivities. There is a certain relationship between the joint hardness and cold crack sensitivity of welded joints; thus, predicting the joint hardness is necessary. Considering the inefficiency of welding experiments and the complexity of welding parameters, we designed a set of processes from temperature field analysis to microstructure prediction and finally hardness prediction. Firstly, we calculated the thermal cycle curve during welding through multi-layer welding numerical simulation using the finite element method (FEM). Afterwards, BP neural networks were used to predict the cooling rates in the temperature interval that ferrite nuclears and grows. Introducing the cooling rates to the Leblond function, the ferrite fraction of the joint was given. Based on the predicted ferrite fraction, mapping relationships between joint hardness and the joint ferrite fraction were built using BP neural networks. The results shows that the error during phase fraction prediction is less than 8%, and during joint hardness prediction, it is less than 5%.https://www.mdpi.com/2073-4352/15/1/14X80 pipeline steelmulti-layer welding numerical simulationBP neural networkphase fraction predictionjoint hardness prediction |
| spellingShingle | Chen Yan Haonan Li Die Yang Yanan Gao Jun Deng Zhihang Zhang Zhibo Dong Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel Crystals X80 pipeline steel multi-layer welding numerical simulation BP neural network phase fraction prediction joint hardness prediction |
| title | Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel |
| title_full | Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel |
| title_fullStr | Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel |
| title_full_unstemmed | Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel |
| title_short | Integrated Prediction of Gas Metal Arc Welding Multi-Layer Welding Heat Cycle, Ferrite Fraction, and Joint Hardness of X80 Pipeline Steel |
| title_sort | integrated prediction of gas metal arc welding multi layer welding heat cycle ferrite fraction and joint hardness of x80 pipeline steel |
| topic | X80 pipeline steel multi-layer welding numerical simulation BP neural network phase fraction prediction joint hardness prediction |
| url | https://www.mdpi.com/2073-4352/15/1/14 |
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