Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments
Considering the complex relationship among mechanical properties of 7xxx aluminum alloy, it is very crucial to optimize two or more target properties simultaneously in developing new materials. In this paper, three different machine learning assisted strategies are used to study the relationship bet...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785422008766 |
| _version_ | 1811281328101916672 |
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| author | B. Li Y. Du Z.S. Zheng X.C. Ye D. Fang X.D. Si Y.Q. Wang |
| author_facet | B. Li Y. Du Z.S. Zheng X.C. Ye D. Fang X.D. Si Y.Q. Wang |
| author_sort | B. Li |
| collection | DOAJ |
| description | Considering the complex relationship among mechanical properties of 7xxx aluminum alloy, it is very crucial to optimize two or more target properties simultaneously in developing new materials. In this paper, three different machine learning assisted strategies are used to study the relationship between alloy composition, process parameters and mechanical properties of 7xxx aluminum alloy, which is expected to accelerate the development of new materials. Firstly, the mechanical properties prediction model of 7xxx aluminum alloy was established by back propagation (BP) neural network. Then, on this basis, genetic algorithm (GA) is used to optimize the prediction accuracy of back propagation (BP) neural network. In addition, a radial basis function (RBF) neural network is used for modeling analysis, and the prediction results of the three models are compared. The results show that back propagation (BP) neural network optimized by genetic algorithm (BP-GA) has higher prediction accuracy than BP and RBF neural network, the coefficient of correlation (R), average absolute relative error (AARE) and root mean squared error (RMSE) value are 0.948, 4.28%, 0.087, respectively. In addition, scanning electron microscope (SEM), electron backscatter diffraction (EBSD), high resolution transmission electron microscope (HRTEM) and the tensile test were used to carry out the related experimental verification work. A large number of fine and dispersed spherical precipitates can be observed after ageing heat treatment. The experimental value is close to the target value, which further confirms that BP neural network model optimized by genetic algorithm can be used to predict and design 7xxx aluminum alloy. |
| first_indexed | 2024-04-13T01:31:41Z |
| format | Article |
| id | doaj.art-1675428c1b6842e2a2ff5ab5b837885d |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-04-13T01:31:41Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-1675428c1b6842e2a2ff5ab5b837885d2022-12-22T03:08:29ZengElsevierJournal of Materials Research and Technology2238-78542022-07-011924832496Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experimentsB. Li0Y. Du1Z.S. Zheng2X.C. Ye3D. Fang4X.D. Si5Y.Q. Wang6College of Mechanical and Power Engineering, China Three Gorges University, Yichang 443002, China; Powder Metallurgy Research Institute, Central South University, Changsha 410083, ChinaPowder Metallurgy Research Institute, Central South University, Changsha 410083, China; Corresponding author.School of Mathematics and Statistics, Central South University, Changsha 410083, ChinaCollege of Mechanical and Power Engineering, China Three Gorges University, Yichang 443002, China; Corresponding author.College of Mechanical and Power Engineering, China Three Gorges University, Yichang 443002, ChinaPowder Metallurgy Research Institute, Central South University, Changsha 410083, ChinaSchool of Mathematics and Statistics, Central South University, Changsha 410083, ChinaConsidering the complex relationship among mechanical properties of 7xxx aluminum alloy, it is very crucial to optimize two or more target properties simultaneously in developing new materials. In this paper, three different machine learning assisted strategies are used to study the relationship between alloy composition, process parameters and mechanical properties of 7xxx aluminum alloy, which is expected to accelerate the development of new materials. Firstly, the mechanical properties prediction model of 7xxx aluminum alloy was established by back propagation (BP) neural network. Then, on this basis, genetic algorithm (GA) is used to optimize the prediction accuracy of back propagation (BP) neural network. In addition, a radial basis function (RBF) neural network is used for modeling analysis, and the prediction results of the three models are compared. The results show that back propagation (BP) neural network optimized by genetic algorithm (BP-GA) has higher prediction accuracy than BP and RBF neural network, the coefficient of correlation (R), average absolute relative error (AARE) and root mean squared error (RMSE) value are 0.948, 4.28%, 0.087, respectively. In addition, scanning electron microscope (SEM), electron backscatter diffraction (EBSD), high resolution transmission electron microscope (HRTEM) and the tensile test were used to carry out the related experimental verification work. A large number of fine and dispersed spherical precipitates can be observed after ageing heat treatment. The experimental value is close to the target value, which further confirms that BP neural network model optimized by genetic algorithm can be used to predict and design 7xxx aluminum alloy.http://www.sciencedirect.com/science/article/pii/S2238785422008766Aluminum alloyAlloy designMachine learningMulti-objective optimizationMechanical properties |
| spellingShingle | B. Li Y. Du Z.S. Zheng X.C. Ye D. Fang X.D. Si Y.Q. Wang Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments Journal of Materials Research and Technology Aluminum alloy Alloy design Machine learning Multi-objective optimization Mechanical properties |
| title | Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| title_full | Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| title_fullStr | Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| title_full_unstemmed | Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| title_short | Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| title_sort | manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments |
| topic | Aluminum alloy Alloy design Machine learning Multi-objective optimization Mechanical properties |
| url | http://www.sciencedirect.com/science/article/pii/S2238785422008766 |
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