A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data
Accelerating design and development of new materials by establishing process-structure-property (PSP) linkages is one of the core contents of materials science. One of the challenges is how to accurately forecast the property by the features including chemical compositions, experiment conditions, an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127521000502 |
| _version_ | 1828967430464995328 |
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| author | Xiaobing Hu Junjie Li Zhijun Wang Jincheng Wang |
| author_facet | Xiaobing Hu Junjie Li Zhijun Wang Jincheng Wang |
| author_sort | Xiaobing Hu |
| collection | DOAJ |
| description | Accelerating design and development of new materials by establishing process-structure-property (PSP) linkages is one of the core contents of materials science. One of the challenges is how to accurately forecast the property by the features including chemical compositions, experiment conditions, and structure information. In this study, with consistent features in statistics and materials science, we proposed a microstructure-informatic strategy to achieve the goal of accurately predicting Vickers hardness of austenitic steels. Feature engineering including correlations analysis, importance ranking and microstructural features extraction was employed to ensure the most information contained in the features related to the property. Through training and comparing six regression models with different input features, we demonstrated that one of the models inputting microstructural features obtained by two-point statistics combined with principal component analysis (PCA) maintains the highest accuracy (absolute error≤13.63 MPa, relative error≤8.86%) and predictive stability (minimum error range). The excellent generalization ability of this model was validated by eight experimental instances unseen in the original dataset. We believe that our strategy can be used to guide future experiments due to its high precision. Most importantly, the strategy can be generalized to predict other mechanical properties controlled by microstructures in more material systems. |
| first_indexed | 2024-12-14T11:50:44Z |
| format | Article |
| id | doaj.art-925d8f232b364b91a061e3e7882b9c26 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-12-14T11:50:44Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-925d8f232b364b91a061e3e7882b9c262022-12-21T23:02:20ZengElsevierMaterials & Design0264-12752021-03-01201109497A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental dataXiaobing Hu0Junjie Li1Zhijun Wang2Jincheng Wang3State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, ChinaCorresponding authors.; State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, ChinaState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, ChinaCorresponding authors.; State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, ChinaAccelerating design and development of new materials by establishing process-structure-property (PSP) linkages is one of the core contents of materials science. One of the challenges is how to accurately forecast the property by the features including chemical compositions, experiment conditions, and structure information. In this study, with consistent features in statistics and materials science, we proposed a microstructure-informatic strategy to achieve the goal of accurately predicting Vickers hardness of austenitic steels. Feature engineering including correlations analysis, importance ranking and microstructural features extraction was employed to ensure the most information contained in the features related to the property. Through training and comparing six regression models with different input features, we demonstrated that one of the models inputting microstructural features obtained by two-point statistics combined with principal component analysis (PCA) maintains the highest accuracy (absolute error≤13.63 MPa, relative error≤8.86%) and predictive stability (minimum error range). The excellent generalization ability of this model was validated by eight experimental instances unseen in the original dataset. We believe that our strategy can be used to guide future experiments due to its high precision. Most importantly, the strategy can be generalized to predict other mechanical properties controlled by microstructures in more material systems.http://www.sciencedirect.com/science/article/pii/S0264127521000502Feature engineeringMicrostructure-informaticTwo-point statisticsProperty prediction |
| spellingShingle | Xiaobing Hu Junjie Li Zhijun Wang Jincheng Wang A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data Materials & Design Feature engineering Microstructure-informatic Two-point statistics Property prediction |
| title | A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data |
| title_full | A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data |
| title_fullStr | A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data |
| title_full_unstemmed | A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data |
| title_short | A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data |
| title_sort | microstructure informatic strategy for vickers hardness forecast of austenitic steels from experimental data |
| topic | Feature engineering Microstructure-informatic Two-point statistics Property prediction |
| url | http://www.sciencedirect.com/science/article/pii/S0264127521000502 |
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