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Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning

  • Machine Learning Applications in Advanced Manufacturing Processes
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Abstract

AZ31 magnesium alloys from different suppliers usually have different chemistry and processing histories, causing variance in their mechanical properties. In this work, we establish the quantitative relationship between alloy compositions, processing parameters, and mechanical properties by machine learning. Based on the artificial neural network (ANN) and support vector machine (SVM) algorithms, two models were built using a dataset with 112 data. Both models achieved good accuracy in predicting yield strength (YS), ultimate tensile strength (UTS), and tensile elongation (EL). To test their generalization ability, a new AZ31 extruded alloy was fabricated, with its chemical composition and processing history being documented. The YS, UTS, and EL of this material were measured and compared with model predictions. Relative errors for YS, UTS, and EL were 5.4%, 23%, and 272% by the ANN model, and 28%, 25%, and 143% by the SVM model, respectively. The reasons for the overestimation of the mechanical properties are discussed.

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References

  1. S. Begum, D.L. Chen, S. Xu, and A.A. Luo, Mater. Sci. Eng. A 517, 334 (2009).

    Article  Google Scholar 

  2. H. Pan, Y. Ren, H. Fu, H. Zhao, L. Wang, X. Meng, and G. Qin, J. Alloys Compd. 663, 321 (2016).

    Article  Google Scholar 

  3. P. Raccuglia, K. Elbert, P. Adler, C. Falk, M. Wenny, A. Mollo, M. Zeller, S. Friedler, J. Schrier, and A. Norquist, Nature 533, 73 (2016).

    Article  Google Scholar 

  4. R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, npj Comput. Mater. 3, 54 (2017).

    Article  Google Scholar 

  5. Y. Zhang and C. Ling, npj Comput. Mater. 4, 25 (2018).

    Article  Google Scholar 

  6. A. Agrawal and A. Choudhary, APL Mater. 4, 053208 (2016).

    Article  Google Scholar 

  7. A. Rovinelli, M. Sangid, H. Proudhon, and W. Ludwig, Npj Comput. Mater. 4, 35 (2018).

    Article  Google Scholar 

  8. Z. Tong, L. Wang, G. Zhu, and X. Zeng, Metall. Mater. Trans. A 50A, 5543 (2019).

    Article  Google Scholar 

  9. C. Shen, C. Wang, X. Wei, Y. Li, S.V.D. Zwaag, and W. Xu, Acta Mater. 179, 201 (2019).

    Article  Google Scholar 

  10. C. Wen, Y. Zhang, C. Wang, D. Xue, Y. Bai, S. Antonov, L. Dai, T. Lookman, and Y. Su, Acta Mater. 170, 109 (2019).

    Article  Google Scholar 

  11. B. DeCost, T. Francis, and E. Holm, Acta Mater. 133, 30 (2017).

    Article  Google Scholar 

  12. Z. Chen and S. Daly, Mater. Sci. Eng. A 736, 61 (2018).

    Article  Google Scholar 

  13. Y. Wang and H. Choo, Acta Mater. 81, 83 (2014).

    Article  Google Scholar 

  14. Y. Chino, M. Mamoru, R. Kishihara, H. Hosokawa, Y. Yamada, C. Wen, K. Shimojima, and H. Iwasaki, Mater. Trans. 43, 2254 (2002).

    Google Scholar 

  15. Y. Wang, C. Chang, C. Lee, H.K. Lin, and J.C. Huang, Scr. Mater. 55, 637 (2006).

    Article  Google Scholar 

  16. S. Liang, Z. Liu, and E. Wang, Rare Met. Mater. Eng. 46, 1411 (2017).

    Google Scholar 

  17. A. Yamashita, Z. Horita, and T. Langdon, Mater. Sci. Eng. A 300, 142 (2001).

    Article  Google Scholar 

  18. S. Liang, X. Wang, and Z. Liu, Rare Met. Mater. Eng. 38, 1276 (2009).

    Google Scholar 

  19. H. Luo, Mater. Mech. Eng. 37, 60 (2013).

    Google Scholar 

  20. L. Shi, J. Li, and Y. Li, Forg. Stamp. Technol. 34, 35 (2009).

    Google Scholar 

  21. P. Wu and X. Dai, Hot Work. Technol. 46, 136 (2017).

    Google Scholar 

  22. C. Zhao, G. Wang, and Y. Huang, Hot Work. Technol. 44, 231 (2015).

    Google Scholar 

  23. H. Lu. Master Thesis. Taiyuan University of Science and Technology (2017).

  24. P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining (New York: Pearson, 2005).

    Google Scholar 

  25. https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html.

  26. K. He, X. Zhang, S. Ren, and J. Sun, Proceedings of the IEEE international conference on computer vision, 1026 (2015).

  27. A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Sebastopol, CA: O’Reilly Media, 2019).

    Google Scholar 

  28. G. Zhu, L. Wang, H. Zhou, J. Wang, Y. Shen, P. Tu, H. Zhu, W. Liu, P. Jin, and X. Zeng, Int. J. Plasticity 120, 164 (2019).

    Article  Google Scholar 

  29. H. Yu, C. Li, Y. Xin, A. Chapuis, X. Huang, and Q. Liu, Acta Mater. 128, 313 (2017).

    Article  Google Scholar 

  30. Z.R. Zeng, Y.M. Zhu, R.L. Liu, S.W. Xu, C.H.J. Davies, J.F. Nie, and N. Birbilis, Acta Mater. 160, 97 (2018).

    Article  Google Scholar 

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Acknowledgements

This work is financially supported by a collaborative research project (No. 18X120010001) between the University of Michigan and Shanghai Jiao Tong University which applies data science to Mg alloy design. We also acknowledge support from the National Key Research and Development Program of China (No. 2016YFB0701203) and the Science and Technology Commission of Shanghai Municipality (No. 18511109302).

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Authors and Affiliations

  1. National Engineering Research Center of Light Alloy Net Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xuenan Xu, Leyun Wang, Gaoming Zhu & Xiaoqin Zeng

  2. Materials Genome Initiative Center, Shanghai Jiao Tong University, Shanghai, 200240, China

    Leyun Wang

  3. State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaoqin Zeng

  4. School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xuenan Xu

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  1. Xuenan Xu
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  2. Leyun Wang
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  3. Gaoming Zhu
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  4. Xiaoqin Zeng
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Correspondence to Leyun Wang.

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Xu, X., Wang, L., Zhu, G. et al. Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning. JOM 72, 3935–3942 (2020). https://doi.org/10.1007/s11837-020-04343-w

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  • DOI: https://doi.org/10.1007/s11837-020-04343-w

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