Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures
Accurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S026412752100681X |
| _version_ | 1818934946381692928 |
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| author | Xingqi Jia Wei Li Qi Lu Kuan Zhang Hao Du Yuantao Xu Xuejun Jin |
| author_facet | Xingqi Jia Wei Li Qi Lu Kuan Zhang Hao Du Yuantao Xu Xuejun Jin |
| author_sort | Xingqi Jia |
| collection | DOAJ |
| description | Accurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments and machine learning on steels for low-temperature service. An algorithm based on a convolutional neural network enriched with the two-dimensional input terminals increased the prediction accuracy, achieving an average microhardness error of as low as 14.37 HV for the validation set. The improved prediction accuracy is ascribed to the comprehensive strengthening mechanism and coupling of strengthening effects contained in the multifarious input terminals. The information acquisition and cross-correlation of substructures related to strengthening mechanism played an important role. The reported strategy can deepen the cognition of the strengthening mechanism of tempered martensite. It is promising for application to different steels containing tempered martensite. |
| first_indexed | 2024-12-20T05:12:21Z |
| format | Article |
| id | doaj.art-d1087622e4f94547b7e29eb6f2e75a40 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-12-20T05:12:21Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-d1087622e4f94547b7e29eb6f2e75a402022-12-21T19:52:15ZengElsevierMaterials & Design0264-12752021-12-01211110126Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructuresXingqi Jia0Wei Li1Qi Lu2Kuan Zhang3Hao Du4Yuantao Xu5Xuejun Jin6Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaCorresponding authors at: Institute of Advanced Steels and Materials, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.; Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaShanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaShanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaShanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaShanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaCorresponding authors at: Institute of Advanced Steels and Materials, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.; Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR ChinaAccurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments and machine learning on steels for low-temperature service. An algorithm based on a convolutional neural network enriched with the two-dimensional input terminals increased the prediction accuracy, achieving an average microhardness error of as low as 14.37 HV for the validation set. The improved prediction accuracy is ascribed to the comprehensive strengthening mechanism and coupling of strengthening effects contained in the multifarious input terminals. The information acquisition and cross-correlation of substructures related to strengthening mechanism played an important role. The reported strategy can deepen the cognition of the strengthening mechanism of tempered martensite. It is promising for application to different steels containing tempered martensite.http://www.sciencedirect.com/science/article/pii/S026412752100681XLaser depositionMartensitic steelsElectron backscattering diffraction (EBSD)HardnessMachine learning |
| spellingShingle | Xingqi Jia Wei Li Qi Lu Kuan Zhang Hao Du Yuantao Xu Xuejun Jin Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures Materials & Design Laser deposition Martensitic steels Electron backscattering diffraction (EBSD) Hardness Machine learning |
| title | Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures |
| title_full | Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures |
| title_fullStr | Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures |
| title_full_unstemmed | Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures |
| title_short | Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures |
| title_sort | towards accurate prediction for ultra low carbon tempered martensite property through the cross correlated substructures |
| topic | Laser deposition Martensitic steels Electron backscattering diffraction (EBSD) Hardness Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S026412752100681X |
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