Modeling the correlation between texture characteristics and tensile properties of AZ31 magnesium alloy based on the artificial neural networks
This work is aimed to investigate the relationship between the texture and tensile properties of the AZ31 Mg alloy by the machine learning method. The texture characteristics parameters, namely the maximum pole intensity (Imax), texture dispersion (D), and texture directivities along the longitudina...
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Published in | Journal of materials research and technology Vol. 24; pp. 5286 - 5297 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.05.2023
Elsevier |
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Abstract | This work is aimed to investigate the relationship between the texture and tensile properties of the AZ31 Mg alloy by the machine learning method. The texture characteristics parameters, namely the maximum pole intensity (Imax), texture dispersion (D), and texture directivities along the longitudinal direction (PLD) and transverse direction (PTD), are extracted from the (0002) pole figures of the AZ31 Mg alloy. An artificial neural networks (ANN) model to describe the relationship between the texture characteristic parameters and tensile properties is constructed and trained by the data collected from the literature. To validate the reliability and generalization performance of the ANN, 6 samples with different texture characteristics are prepared, and their textures and tensile properties are evaluated through electron backscattered diffraction (EBSD) measurement and uniaxial tensile test, respectively. The results indicate that the ANN model exhibits good prediction performance in yield strength and elongation of the AZ31 Mg alloy when it is applied to the new cases. The correlations between the texture characteristics and tensile properties are analyzed according to the ANN-predicted results. The maximum pole intensity and texture dispersion significantly influence the tensile properties of the AZ31 Mg alloy. With increasing the Imax or decreasing the D, the strength is increased but the elongation is reduced. As increasing the texture directivity along the LD, the tensile properties of the AZ31 Mg alloy show non-monotonic changes. This research presents a correlation model between the texture and mechanical properties of the Mg alloy, which contributes to the development of high-performance Mg alloys. |
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AbstractList | This work is aimed to investigate the relationship between the texture and tensile properties of the AZ31 Mg alloy by the machine learning method. The texture characteristics parameters, namely the maximum pole intensity (Imax), texture dispersion (D), and texture directivities along the longitudinal direction (PLD) and transverse direction (PTD), are extracted from the (0002) pole figures of the AZ31 Mg alloy. An artificial neural networks (ANN) model to describe the relationship between the texture characteristic parameters and tensile properties is constructed and trained by the data collected from the literature. To validate the reliability and generalization performance of the ANN, 6 samples with different texture characteristics are prepared, and their textures and tensile properties are evaluated through electron backscattered diffraction (EBSD) measurement and uniaxial tensile test, respectively. The results indicate that the ANN model exhibits good prediction performance in yield strength and elongation of the AZ31 Mg alloy when it is applied to the new cases. The correlations between the texture characteristics and tensile properties are analyzed according to the ANN-predicted results. The maximum pole intensity and texture dispersion significantly influence the tensile properties of the AZ31 Mg alloy. With increasing the Imax or decreasing the D, the strength is increased but the elongation is reduced. As increasing the texture directivity along the LD, the tensile properties of the AZ31 Mg alloy show non-monotonic changes. This research presents a correlation model between the texture and mechanical properties of the Mg alloy, which contributes to the development of high-performance Mg alloys. |
Author | Li, Kun Jiang, Bin Zhang, Yibing Pan, Fusheng Dong, Zhihua Bai, Shengwen |
Author_xml | – sequence: 1 givenname: Yibing surname: Zhang fullname: Zhang, Yibing organization: National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing, 400044, China – sequence: 2 givenname: Shengwen orcidid: 0000-0002-4717-4724 surname: Bai fullname: Bai, Shengwen email: baisw@cqu.edu.cn organization: National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing, 400044, China – sequence: 3 givenname: Bin surname: Jiang fullname: Jiang, Bin email: jiangbinrong@cqu.edu.cn organization: National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing, 400044, China – sequence: 4 givenname: Kun orcidid: 0000-0001-9100-0302 surname: Li fullname: Li, Kun organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China – sequence: 5 givenname: Zhihua surname: Dong fullname: Dong, Zhihua organization: National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing, 400044, China – sequence: 6 givenname: Fusheng surname: Pan fullname: Pan, Fusheng organization: National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing, 400044, China |
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Keywords | Texture characteristics Tensile properties Magnesium alloy Artificial neural networks |
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SubjectTerms | Artificial neural networks Magnesium alloy Tensile properties Texture characteristics |
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Title | Modeling the correlation between texture characteristics and tensile properties of AZ31 magnesium alloy based on the artificial neural networks |
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