Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning
Traditional trial-and-error methods for optimizing the composition of heat-resistant aluminum alloys often consume significant time and resources, making it difficult to achieve alloys with excellent mechanical properties. This study combines experimental and machine learning methods to predict the...
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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/S0264127525000073 |
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| author | Changmei Hao Yudong Sui Yanru Yuan Pengfei Li Haini Jin Aoyang Jiang |
| author_facet | Changmei Hao Yudong Sui Yanru Yuan Pengfei Li Haini Jin Aoyang Jiang |
| author_sort | Changmei Hao |
| collection | DOAJ |
| description | Traditional trial-and-error methods for optimizing the composition of heat-resistant aluminum alloys often consume significant time and resources, making it difficult to achieve alloys with excellent mechanical properties. This study combines experimental and machine learning methods to predict the optimal alloy composition for maximum ultimate tensile strength(UTS) at 300 °C and 350 °C. The AdaBoost algorithm was chosen as the final model. Experimental results show that predictions of the machine learning model deviate by only 7.75 % from the actual results, with an R2 of 0.94. Furthermore, the study found that Al9FeNi and Al3Ni play key roles in enhancing the high-temperature mechanical properties of cast heat-resistant aluminum alloys. This model accurately predicts the high-temperature mechanical performance of heat-resistant aluminum alloys, providing effective guidance for their composition design. |
| format | Article |
| id | doaj-art-5afc56c0691c44ec9f5331f08f8d09c0 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-5afc56c0691c44ec9f5331f08f8d09c02025-01-11T06:38:25ZengElsevierMaterials & Design0264-12752025-02-01250113587Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learningChangmei Hao0Yudong Sui1Yanru Yuan2Pengfei Li3Haini Jin4Aoyang Jiang5School of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; National-Local Joint Engineering Research Center for Technology of Advanced Metallic Solidification Forming and Equipment, Kunming University of Science and Technology, PR ChinaSchool of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; National-Local Joint Engineering Research Center for Technology of Advanced Metallic Solidification Forming and Equipment, Kunming University of Science and Technology, PR China; Corresponding authors at: School of Materials Science and Engineering, Kunming University of Science and Technology, 253, Xuefu Road, Kunming 650093, PR ChinaSchool of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, PR ChinaSchool of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; National-Local Joint Engineering Research Center for Technology of Advanced Metallic Solidification Forming and Equipment, Kunming University of Science and Technology, PR ChinaSchool of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; National-Local Joint Engineering Research Center for Technology of Advanced Metallic Solidification Forming and Equipment, Kunming University of Science and Technology, PR ChinaSchool of Electrical Engineering, Sichuan University, Chengdu 610000, PR China; Corresponding authors at: School of Electrical Engineering, Sichuan University, Chengdu 610000, PR ChinaTraditional trial-and-error methods for optimizing the composition of heat-resistant aluminum alloys often consume significant time and resources, making it difficult to achieve alloys with excellent mechanical properties. This study combines experimental and machine learning methods to predict the optimal alloy composition for maximum ultimate tensile strength(UTS) at 300 °C and 350 °C. The AdaBoost algorithm was chosen as the final model. Experimental results show that predictions of the machine learning model deviate by only 7.75 % from the actual results, with an R2 of 0.94. Furthermore, the study found that Al9FeNi and Al3Ni play key roles in enhancing the high-temperature mechanical properties of cast heat-resistant aluminum alloys. This model accurately predicts the high-temperature mechanical performance of heat-resistant aluminum alloys, providing effective guidance for their composition design.http://www.sciencedirect.com/science/article/pii/S0264127525000073Machine learningHeat-resistant aluminum alloyMechanical propertiesComposition optimization design |
| spellingShingle | Changmei Hao Yudong Sui Yanru Yuan Pengfei Li Haini Jin Aoyang Jiang Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning Materials & Design Machine learning Heat-resistant aluminum alloy Mechanical properties Composition optimization design |
| title | Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning |
| title_full | Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning |
| title_fullStr | Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning |
| title_full_unstemmed | Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning |
| title_short | Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning |
| title_sort | composition optimization design and high temperature mechanical properties of cast heat resistant aluminum alloy via machine learning |
| topic | Machine learning Heat-resistant aluminum alloy Mechanical properties Composition optimization design |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525000073 |
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