Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning
Abstract Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learni...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-03-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.26 |
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| author | Longfei Zhu Qun Luo Qiaochuan Chen Yu Zhang Lijun Zhang Bin Hu Yuexing Han Qian Li |
| author_facet | Longfei Zhu Qun Luo Qiaochuan Chen Yu Zhang Lijun Zhang Bin Hu Yuexing Han Qian Li |
| author_sort | Longfei Zhu |
| collection | DOAJ |
| description | Abstract Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys. |
| format | Article |
| id | doaj-art-765670d8139346c0aa4d8fe6a5f98cfd |
| institution | OA Journals |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-765670d8139346c0aa4d8fe6a5f98cfd2025-08-20T02:11:34ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-03-0121n/an/a10.1002/mgea.26Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learningLongfei Zhu0Qun Luo1Qiaochuan Chen2Yu Zhang3Lijun Zhang4Bin Hu5Yuexing Han6Qian Li7State Key Laboratory of Advanced Special Steels & Shanghai Key Laboratory of Advanced Ferrometallurgy & School of Materials Science and Engineering Shanghai University Shanghai ChinaState Key Laboratory of Advanced Special Steels & Shanghai Key Laboratory of Advanced Ferrometallurgy & School of Materials Science and Engineering Shanghai University Shanghai ChinaSchool of Computer Engineering and Science Shanghai University Shanghai ChinaCollege of Materials Science and Engineering Chongqing University Chongqing ChinaState Key Laboratory of Powder Metallurgy Central South University Changsha ChinaCollege of Materials Science and Engineering Chongqing University Chongqing ChinaSchool of Computer Engineering and Science Shanghai University Shanghai ChinaState Key Laboratory of Advanced Special Steels & Shanghai Key Laboratory of Advanced Ferrometallurgy & School of Materials Science and Engineering Shanghai University Shanghai ChinaAbstract Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys.https://doi.org/10.1002/mgea.26Al‐Si alloysimage processingmachine learningmultimodalproperty prediction |
| spellingShingle | Longfei Zhu Qun Luo Qiaochuan Chen Yu Zhang Lijun Zhang Bin Hu Yuexing Han Qian Li Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning Materials Genome Engineering Advances Al‐Si alloys image processing machine learning multimodal property prediction |
| title | Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning |
| title_full | Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning |
| title_fullStr | Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning |
| title_full_unstemmed | Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning |
| title_short | Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning |
| title_sort | prediction of ultimate tensile strength of al si alloys based on multimodal fusion learning |
| topic | Al‐Si alloys image processing machine learning multimodal property prediction |
| url | https://doi.org/10.1002/mgea.26 |
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