Toward super-clean bearing steel by a novel physical-data integrated design strategy
The cleanliness of fabricated ingots is crucial for the quality and properties of bearing steel. To address this issue, a physical-data integrated design strategy was developed to optimize vacuum arc remelting (VAR) parameters, combining numerical simulation, machine learning (ML), and experimental...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525000498 |
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| author | Jian Guan Guolei Liu Wenguang Hu Hongwei Liu Paixian Fu Yanfei Cao Dong-Rong Liu Dianzhong Li |
| author_facet | Jian Guan Guolei Liu Wenguang Hu Hongwei Liu Paixian Fu Yanfei Cao Dong-Rong Liu Dianzhong Li |
| author_sort | Jian Guan |
| collection | DOAJ |
| description | The cleanliness of fabricated ingots is crucial for the quality and properties of bearing steel. To address this issue, a physical-data integrated design strategy was developed to optimize vacuum arc remelting (VAR) parameters, combining numerical simulation, machine learning (ML), and experimental validation. Initially, a multi-phase, multi-physics coupled model was developed to predict the movement and distribution of inclusions during the VAR process. Furthermore, five ML algorithms were utilized to predict the cleanliness assessment score (CAS) based on inclusion size and distribution data from various VAR processing parameters, with gradient boosting regression (GBR) showing the best performance. Finally, a systematic framework based on a genetic algorithm was proposed to select the optimal combination of CAS. Here, the ML-optimized processing parameters comprised current of 4255 A, helium pressure of 0.69 kPa, and melting rate of 2.5 kg/min. Intriguingly, the number density of small inclusions at the center of the ingot decreased by 58.2 % and that of large inclusions reduced by 13.3 %. This was mainly caused by the appropriate maximum flow velocity of 2.6–2.8 cm/s during the steady-state stage of the molten pool. This study highlights a common and novel method for fabricating bearing steel with other superalloys via a physical-data integrated strategy. |
| format | Article |
| id | doaj-art-20af1f80eae74566a7102c38fbf077fc |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-20af1f80eae74566a7102c38fbf077fc2025-01-21T04:12:50ZengElsevierMaterials & Design0264-12752025-02-01250113629Toward super-clean bearing steel by a novel physical-data integrated design strategyJian Guan0Guolei Liu1Wenguang Hu2Hongwei Liu3Paixian Fu4Yanfei Cao5Dong-Rong Liu6Dianzhong Li7Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, China; School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, No.4 Linyuan Road, Harbin 150040, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, China; School of Materials Science and Engineering, University of Science and Technology of China, No.96 Jinzhai Road, Hefei 230026, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, China; School of Materials Science and Engineering, University of Science and Technology of China, No.96 Jinzhai Road, Hefei 230026, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, China; Corresponding authors.School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, No.4 Linyuan Road, Harbin 150040, China; Corresponding authors.Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, No.72 Wenhua Road, Shenyang 110016, ChinaThe cleanliness of fabricated ingots is crucial for the quality and properties of bearing steel. To address this issue, a physical-data integrated design strategy was developed to optimize vacuum arc remelting (VAR) parameters, combining numerical simulation, machine learning (ML), and experimental validation. Initially, a multi-phase, multi-physics coupled model was developed to predict the movement and distribution of inclusions during the VAR process. Furthermore, five ML algorithms were utilized to predict the cleanliness assessment score (CAS) based on inclusion size and distribution data from various VAR processing parameters, with gradient boosting regression (GBR) showing the best performance. Finally, a systematic framework based on a genetic algorithm was proposed to select the optimal combination of CAS. Here, the ML-optimized processing parameters comprised current of 4255 A, helium pressure of 0.69 kPa, and melting rate of 2.5 kg/min. Intriguingly, the number density of small inclusions at the center of the ingot decreased by 58.2 % and that of large inclusions reduced by 13.3 %. This was mainly caused by the appropriate maximum flow velocity of 2.6–2.8 cm/s during the steady-state stage of the molten pool. This study highlights a common and novel method for fabricating bearing steel with other superalloys via a physical-data integrated strategy.http://www.sciencedirect.com/science/article/pii/S0264127525000498Vacuum arc remelting (VAR)InclusionsMulti-phase modelGenetic algorithm (GA)Physical-data integrated design strategy |
| spellingShingle | Jian Guan Guolei Liu Wenguang Hu Hongwei Liu Paixian Fu Yanfei Cao Dong-Rong Liu Dianzhong Li Toward super-clean bearing steel by a novel physical-data integrated design strategy Materials & Design Vacuum arc remelting (VAR) Inclusions Multi-phase model Genetic algorithm (GA) Physical-data integrated design strategy |
| title | Toward super-clean bearing steel by a novel physical-data integrated design strategy |
| title_full | Toward super-clean bearing steel by a novel physical-data integrated design strategy |
| title_fullStr | Toward super-clean bearing steel by a novel physical-data integrated design strategy |
| title_full_unstemmed | Toward super-clean bearing steel by a novel physical-data integrated design strategy |
| title_short | Toward super-clean bearing steel by a novel physical-data integrated design strategy |
| title_sort | toward super clean bearing steel by a novel physical data integrated design strategy |
| topic | Vacuum arc remelting (VAR) Inclusions Multi-phase model Genetic algorithm (GA) Physical-data integrated design strategy |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525000498 |
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