Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches
Abstract Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing machine learning (ML) studies often focus on specific steel grades, limiting generalizability in predicting and optimizing AHSS properties. Her...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00786-w |
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| author | Tianyang Li Zheng Yang Junyi Cui Wenjie Chen Rami Almatani Yingjie Wu |
| author_facet | Tianyang Li Zheng Yang Junyi Cui Wenjie Chen Rami Almatani Yingjie Wu |
| author_sort | Tianyang Li |
| collection | DOAJ |
| description | Abstract Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing machine learning (ML) studies often focus on specific steel grades, limiting generalizability in predicting and optimizing AHSS properties. Here, an ML framework was presented to predict and optimize the stretch-flangeability of AHSS based on composition-microstructure-property correlations, using datasets from 212 steel conditions. Support vector machine, symbolic regression, and extreme gradient boosting models accurately predicted hole expansion ratio (HER), ultimate tensile strength (UTS), and total elongation (TE). Shapley additive explanations revealed the importance of bainite volume fraction (VB), carbon content (C), and chromium content (Cr) for HER, UTS, and TE, respectively. Multi-objective optimization generated 252 optimized conditions with improved comprehensive mechanical properties. The best optimized chemical compositions (0.12wt.% C-1.10Mn-0.15Si-0.47Cr) along with the carbon equivalent (CE) of 0.44 wt.%, and microstructural features (7.2% ferrite, 44.5% bainite, 40.5% martensite, and 7.8% tempered martensite) yielded HER of 119.8%, UTS of 1013.5 MPa, and TE of 22.7%. This systematic framework enables efficient prediction and optimization of material properties (especially HER), with potential applications across various fields of materials science. |
| format | Article |
| id | doaj-art-aba82968ff394389ba3003da1e97afcc |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-aba82968ff394389ba3003da1e97afcc2025-08-20T03:09:34ZengNature PortfolioScientific Reports2045-23222025-05-0115112110.1038/s41598-025-00786-wPrediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approachesTianyang Li0Zheng Yang1Junyi Cui2Wenjie Chen3Rami Almatani4Yingjie Wu5Sichuan University - Pittsburgh Institute (SCUPI), Sichuan UniversitySichuan University - Pittsburgh Institute (SCUPI), Sichuan UniversityDepartment of Materials Science and Engineering, National University of SingaporeSichuan University - Pittsburgh Institute (SCUPI), Sichuan UniversityAdvanced Materials Technologies Institute, King Abdulaziz City for Science and TechnologySichuan University - Pittsburgh Institute (SCUPI), Sichuan UniversityAbstract Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing machine learning (ML) studies often focus on specific steel grades, limiting generalizability in predicting and optimizing AHSS properties. Here, an ML framework was presented to predict and optimize the stretch-flangeability of AHSS based on composition-microstructure-property correlations, using datasets from 212 steel conditions. Support vector machine, symbolic regression, and extreme gradient boosting models accurately predicted hole expansion ratio (HER), ultimate tensile strength (UTS), and total elongation (TE). Shapley additive explanations revealed the importance of bainite volume fraction (VB), carbon content (C), and chromium content (Cr) for HER, UTS, and TE, respectively. Multi-objective optimization generated 252 optimized conditions with improved comprehensive mechanical properties. The best optimized chemical compositions (0.12wt.% C-1.10Mn-0.15Si-0.47Cr) along with the carbon equivalent (CE) of 0.44 wt.%, and microstructural features (7.2% ferrite, 44.5% bainite, 40.5% martensite, and 7.8% tempered martensite) yielded HER of 119.8%, UTS of 1013.5 MPa, and TE of 22.7%. This systematic framework enables efficient prediction and optimization of material properties (especially HER), with potential applications across various fields of materials science.https://doi.org/10.1038/s41598-025-00786-wStretch-flangeabilityAdvanced high strength steelsComposition-structure-property relationshipMachine learningMultiple objective optimization |
| spellingShingle | Tianyang Li Zheng Yang Junyi Cui Wenjie Chen Rami Almatani Yingjie Wu Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches Scientific Reports Stretch-flangeability Advanced high strength steels Composition-structure-property relationship Machine learning Multiple objective optimization |
| title | Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| title_full | Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| title_fullStr | Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| title_full_unstemmed | Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| title_short | Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| title_sort | prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches |
| topic | Stretch-flangeability Advanced high strength steels Composition-structure-property relationship Machine learning Multiple objective optimization |
| url | https://doi.org/10.1038/s41598-025-00786-w |
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