Bogdan Nenchev, Qing Tao, Zihui Dong, Chinnapat Panwisawas, Haiyang Li, Biao Tao, and Hongbiao Dong, Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 836-847. https://doi.org/10.1007/s12613-022-2437-0
Cite this article as:
Bogdan Nenchev, Qing Tao, Zihui Dong, Chinnapat Panwisawas, Haiyang Li, Biao Tao, and Hongbiao Dong, Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 836-847. https://doi.org/10.1007/s12613-022-2437-0
Research Article

Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability

+ Author Affiliations
  • Corresponding author:

    Hongbiao Dong    E-mail: h.dong@le.ac.uk

  • Received: 12 December 2021Revised: 27 January 2022Accepted: 11 February 2022Available online: 12 February 2022
  • Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a case study on gear steel hardenability. The limitations of current data-driven algorithms and empirical models are identified. Challenges in analysing small datasets are discussed, and solution is proposed to handle small datasets with multiple variables. Gaussian methods in combination with novel predictive algorithms are utilized to overcome the challenges in analysing gear steel hardenability data and to gain insight into alloying elements interaction and structure homogeneity. The gained fundamental knowledge integrated with machine learning is shown to be superior to the empirical equations in predicting hardenability. Metallurgical-property relationships between chemistry, sample size, and hardness are predicted via two optimized machine learning algorithms: neural networks (NNs) and extreme gradient boosting (XGboost). A comparison is drawn between all algorithms, evaluating their performance based on small data sets. The results reveal that XGboost has the highest potential for predicting hardenability using small datasets with class imbalance and large inhomogeneity issues.
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  • [1]
    M. Vctor Li, D.V. Niebuhr, L.L. Meekisho, and D.G. Atteridge, A computational model for the prediction of steel hardenability, Metall. Mater. Trans. B, 29(1998), No. 3, p. 661. doi: 10.1007/s11663-998-0101-3
    [2]
    V. Javaheri, A. Pohjonen, J.I. Asperheim, D. Ivanov, and D. Porter, Physically based modeling, characterization and design of an induction hardening process for a new slurry pipeline steel, Mater. Des., 182(2019), art. No. 108047. doi: 10.1016/j.matdes.2019.108047
    [3]
    E.C.H.C. O’ Brien and H.K. Yeddu, Multi-length scale modeling of carburization, martensitic microstructure evolution and fatigue properties of steel gears, J. Mater. Sci. Technol., 49(2020), p. 157. doi: 10.1016/j.jmst.2019.10.044
    [4]
    P.H. Maynier, J. Dollet, and P. Bastien. Prediction of microstructure via empirical formulas based on CCT diagrams, [in] The 107th AIME Annual Meeting, Denver, Colorado, 1978, p. 163.
    [5]
    D. Khan and B. Gautham, Integrated modeling of carburizing-quenching-tempering of steel gears for an ICME framework, Integr. Mater. Manuf. Innovation, 7(2018), No. 1, p. 28. doi: 10.1007/s40192-018-0107-x
    [6]
    S. Feng, H.Y. Zhou, and H.B. Dong, Using deep neural network with small dataset to predict material defects, Mater. Des., 162(2019), p. 300. doi: 10.1016/j.matdes.2018.11.060
    [7]
    C.G. Shen, C.C. Wang, X.L. Wei, Y. Li, S. van der Zwaag, and W. Xu, Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel, Acta Mater., 179(2019), p. 201. doi: 10.1016/j.actamat.2019.08.033
    [8]
    F.E. Bock, R.C. Aydin, C.J. Cyron, N. Huber, S.R. Kalidindi, and B. Klusemann, A review of the application of machine learning and data mining approaches in continuum materials mechanics, Front. Mater., 6(2019), art. No. 00110. doi: 10.3389/fmats.2019.00110
    [9]
    H.K.D.H. Bhadeshia, Neural networks in materials science, ISIJ Int., 39(1999), No. 10, p. 966. doi: 10.2355/isijinternational.39.966
    [10]
    S.W. Wu, J. Yang, and G.M. Cao, Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1309. doi: 10.1007/s12613-020-2168-z
    [11]
    Z.H. Deng, H.Q. Yin, X. Jiang, C. Zhang, G.F. Zhang, B. Xu, G.Q. Yang, T. Zhang, M. Wu, and X.H. Qu, Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy, Int. J. Miner. Metall. Mater., 27(2020), No. 3, p. 362. doi: 10.1007/s12613-019-1894-6
    [12]
    J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors), Ann. Stat., 28(2000), No. 2, p. 337. doi: 10.1214/aos/1016218223
    [13]
    T.Q. Chen and C. Guestrin, XGBoost: A scalable tree boosting system [in] Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 2016, p.785
    [14]
    J. Bennett and S. Lanning, The Netflix prize, [in] Proceedings of KDD Cup and Workshop 2007, San Jose, 2007.
    [15]
    K. Song, F. Yan, T. Ding, L. Gao, and S.B. Lu, A steel property optimization model based on the XGBoost algorithm and improved PSO, Comput. Mater. Sci., 174(2020), art. No. 109472. doi: 10.1016/j.commatsci.2019.109472
    [16]
    E.T. Akinlabi1, O.M. Ikumapayi, O.P. Bodunde, B.A. Adaramola, I.D. Uchegbu, and S.O. Fatoba, Impact of quenching on the hardenability of steels EN-3 (~1015), EN-8 (~1040) and EN-24 (~4340) during Jominy end quench technique. Int. J. Emerging Technol. 11(2020), No. 5, p. 290.
    [17]
    F. Wetschoreck, T. Krabel, and S. Krishnamurthy, 8080labs/Ppscore: Zenodo Release [2020-10-15]. DOI: 10.5281/zenodo.4091345
    [18]
    R.A. Waltz, J.L. Morales, J. Nocedal, and D. Orban, An interior algorithm for nonlinear optimization that combines line search and trust region steps, Math. Program., 107(2006), No. 3, p. 391. doi: 10.1007/s10107-004-0560-5
    [19]
    P. Schüler, Calculation of hardenability in the Jominy end quench test on the basis of the Chemical composition of steel, Revue de Métallurgie, 89(1992), No. 1, p. 93.
    [20]
    F. Burden and D. Winkler, Bayesian regularization of neural networks, [in] D.J. Livingstone ed, Artificial Neural Networks, Methods in Molecular Biology™, Humana Press, 458(2008), p. 23.
    [21]
    S. Feng, H.Y. Zhou, and H.B. Dong, Application of deep transfer learning to predicting crystal structures of inorganic substances, Comput. Mater. Sci., 195(2021), art. No. 110476. doi: 10.1016/j.commatsci.2021.110476
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