Skip to main content
SpringerLink
Log in

Accelerated Development of High-Strength Magnesium Alloys by Machine Learning

  • Original Research Article
  • Published:
Metallurgical and Materials Transactions A Aims and scope Submit manuscript

Abstract

Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability Statement

Supplementary data to this article can be found online at https://jbox.sjtu.edu.cn/l/5odMQf.

References

  1. [1]S. Celotto: Acta Mater., 2000, vol. 48, pp. 1775-1787.

    Article  CAS  Google Scholar 

  2. [2]J. Wang, N. Stanford: Acta Mater., 2015, vol. 100, pp. 53-63.

    Article  CAS  Google Scholar 

  3. [3]J. Jayaraj, C.L. Mendis, T. Ohkubo, K. Oh-ishi and K. Hono: Scripta Mater., 2010, vol. 63, pp. 831-834.

    Article  CAS  Google Scholar 

  4. [4]J.F. Nie and B. C. Muddle: Acta Mater., 2000, vol. 48, pp. 1691-1703.

    Article  CAS  Google Scholar 

  5. [5]J. Jain, P. Cizek, W.J. Poole, M.R. Barnett: Acta Mater., 2013, vol. 61, pp. 4091-4102.

    Article  CAS  Google Scholar 

  6. Y. Kang, H. Yan, R. Chen: Mater. Sci. Eng. A, 2015, vol. 646, pp. 361-368.

    Article  Google Scholar 

  7. [7]C.Q. Liu, H.W. Chen, H. Liu, X.J. Zhao, J.F. Nie: Acta Mater., 2018, vol. 144, pp.590-600.

    Article  CAS  Google Scholar 

  8. [8]J.F. Nie: Metall. Mater. Trans. A, 2012, vol. 43, pp. 3891-3939.

    Article  Google Scholar 

  9. [9]Q. Luo, Y. Guo, B. Liu, Y. Feng, J. Zhang, Q. Li, and K. Chou: J. Mater. Sci. Technol., 2020, vol. 44, pp. 171-190.

    Article  Google Scholar 

  10. [10]L. Deng and X. Li: IEEE Trans. Audio Speech Lang. Process., 2013, vol. 21, pp. 1060-1088.

    Article  Google Scholar 

  11. K. He, X. Zhang, S. Ren, J. Sun: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–78.

  12. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa: J. Mach. Learn Res., 2011, pp. 2493–2537.

  13. [13]P. Raccuglia, K. Elbert, P. Adler, C. Falk, M. Wenny, A. Mollo, M. Zeller, S. Friedler, and J. Schrier: Nature, 2016, vol. 533, pp. 73-76.

    Article  CAS  Google Scholar 

  14. [14]X. Xu, L. Wang, G. Zhu, X. Zeng: JOM, 2020, vol. 72, pp. 3935-3942.

    Article  CAS  Google Scholar 

  15. [15]R. Ramprasad, R. Batra, and G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim: npj Comput. Mater., 2017, vol. 3, pp. 1-13.

    Article  Google Scholar 

  16. [16]A. Agrawal and A. Choudhary: APL Materials., 2016, vol. 4, p. 053208.

    Article  Google Scholar 

  17. [17]A. Rovinelli, M. Sangid, H. Proudhon, and W. Ludwig: npj Comput. Mater., 2018, vol. 4, pp. 1-10.

    Article  Google Scholar 

  18. [18]C. Shen, C. Wang, X. Wei, Y. Li, S. V.D. Zwaag, and W. Xu: Acta Mater., 2019, vol. 179, pp. 201-214.

    Article  CAS  Google Scholar 

  19. [19]C. Wen, Y. Zhang, C. Wang, D. Xue, Y. Bai, S. Antonov, L. Dai, T. Lookman, and Y. Su: Acta Mater., 2019, vol. 170, pp. 109-117.

    Article  CAS  Google Scholar 

  20. [20]C. Wang, H. Fu, L. Jiang, D. Xue, and J. Xie: npj Comput. Mater., 2019, vol. 5, pp. 1-8.

    Article  Google Scholar 

  21. [21]B. DeCost, T. Francis, and E. Holm: Acta Mater., 2017, vol. 133, pp. 30-40.

    Article  CAS  Google Scholar 

  22. [22]D. Xue, P. V. Balachandran, J. Hogden, J. Theiler, D. Xue, and T. Lookman: Nature Commun., 2016, vol. 7, pp. 1-9.

    Google Scholar 

  23. [23]D. Xue, D. Xue, R. Yan, Y. Zhou, and P. V. Balachandran, X. Dong, J. Sun, and T. Lookman: Acta Mater., 2017, vol. 125, pp. 532-541.

    Article  CAS  Google Scholar 

  24. [24]T. Lookman, P. V. Balachandran, D. Xue, and R. Yuan: npj Comput. Mater., 2019, vol. 5, pp. 1-17.

    Article  Google Scholar 

  25. https://jbox.sjtu.edu.cn/l/5odMQf

  26. [26]E. Schubert, J. Sander, M. Ester, H.P. Kriegel, and X. Xu: ACM Transactions on Database Systems, 2017, vol. 42, pp. 1-21.

    Article  Google Scholar 

  27. [27]C. Liu, H. Chen, C. He, Y. Zhang, and J.F. Nie: Mater. Charact., 2016, vol. 113, pp. 214-221.

    Article  CAS  Google Scholar 

  28. [28]R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, C. Kim: npj Comput. Mater., 2017, vol. 3(1), pp. 1-13.

    Article  Google Scholar 

  29. [29]P.N. Tan, M. Steinbach, and V. Kumar: Introduction to Data Mining, Person, New York, 2005.

    Google Scholar 

  30. [30]T. Chen, and C. Guestrin: XGBoosting: A Scalable Tree Boosting System, ACM, San Francisco, 2016, pp. 785-794.

    Google Scholar 

  31. [31]K. Zhang, H. Li, X. Liang, Z. Chen, and L. Wang: Mater. Charact., 2020, vol. 161, p. 110146.

    Article  CAS  Google Scholar 

  32. [32]X. Huang, and S. Huang: JOM, 2020, vol. 72, pp. 1384-1394.

    Article  CAS  Google Scholar 

  33. [33]L. Kaelbling: Learning in Embedded Systems, MIT press, Cambridge, MA, 1993

    Book  Google Scholar 

  34. [34]D.R. Jones, M. Schonlau, and W.J. Welch: J. Global Optim., 1998, vol. 13, pp. 455-492.

    Article  Google Scholar 

  35. [35]Y. Wang, Y. Tian, T. Kirk, O. Laris, J. H.Ross Jr., R. D.Noebe, V. Keylin, R. Arroyave: Acta Mater., 2020, vol. 194, pp. 144-155.

    Article  CAS  Google Scholar 

  36. T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, Z. Zhang. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 7, pp. 842–50.

  37. A. Viswanath, H. Dieringa, K.K. AjithKumar, U.T.S. Pillai, and B. C. Pai: J. Magn. Alloy, 2015, vol. 3, pp. 16-22.

    Article  CAS  Google Scholar 

  38. [38]Y. Xu, X. Zeng, K. Wang, and B. Huang: Philos. Mag., 2015, vol. 95, pp. 1626-1645.

    Article  Google Scholar 

  39. F.R. Elsayed, T.T. Sasaki, C.L. Mendis, T. Ohkubo, and K. Hono: Mater. Sci. Eng. A, 2013, vol. 566, pp. 22-29.

    Article  CAS  Google Scholar 

  40. [40]S. Jo, S. Kim, T. Kim, Y. Go, C. Yang, B. You, Y. Kim: J. Alloy Compd., 2018, vol. 749, pp. 794-802.

    Article  CAS  Google Scholar 

  41. [41]D. Duly, W.Z. Zhang, and M. Audier: Phil. Mag. A, 1995, vol. 71, pp. 187-204.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work has been financially supported by the National Key Research and Development Program of China (No. 2016YFB0701203) and a collaborative research project (No. 18X120010001) between University of Michigan and Shanghai Jiao Tong University. L.W. is sponsored by the Youth Cheung Kong Scholars Program (No. Q2018077) and the Shanghai Rising-Star Program (No. 20QA1405000). X.Z. is partly supported by the National Natural Science Foundation of China (No. 51825101).

Author information

Authors and Affiliations

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

    Yanwei Liu, Leyun Wang, Huan Zhang, Gaoming Zhu, Jie Wang & Xiaoqin Zeng

  2. State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China

    Yuhui Zhang

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

    Leyun Wang

Authors
  1. Yanwei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leyun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gaoming Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuhui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoqin Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leyun Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Manuscript submitted August 24, 2020; accepted December 14, 2020.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Wang, L., Zhang, H. et al. Accelerated Development of High-Strength Magnesium Alloys by Machine Learning. Metall Mater Trans A 52, 943–954 (2021). https://doi.org/10.1007/s11661-020-06132-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11661-020-06132-1

Search

Navigation