Abstract
In this study, three machine learning (ML) models were developed to predict the secondary dendrite arm spacing (SDAS) and then predictions were validated experimentally. First, a three-layer artificial neural network (ANN) was built to predict the SDAS. Then, a linear regression model (LR) with backward selection method is applied to study the relationship of different elemental properties, processing parameters, and SDAS and make a prediction. A principle component analysis (PCA) further explores these relationships. The results show that the ANN model has the best performance compared with the LR and PCA models. Compared with the classical coarsening equation, the current SDAS predictions reveal a deviation from nearly linear relationship with the negative cubic root of cooling rate, which indicates there are other elemental properties that should be accounted for.
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M. Easton, C. Davidson and D. St John, Metall. Mater. Trans. A, 41, 1528–38 (2010)
R. Grugel, Journal of materials science 28, 677–683 (1993)
L. Dobrzaski, W. Borek, R. Maniara, Journal of Achievements in Materials and Manufacturing. Engineering 18, 211–214 (2006)
D. Kirkwood, Materials Science and Engineering 73, L1–L4 (1985)
M. Chen, T. Kattamis, Materials Science and Engineering: A 247, 239–247 (1998)
M. Ode, S.G. Kim, W.T. Kim, T. Suzuki, ISIJ international 41, 345–349 (2001)
A. Roosz, E. Halder, H. Exner, Materials science and technology 2, 1149–1155 (1986)
J.-W. Yeh, S.-K. Chen, S.-J. Lin, J.-Y. Gan, T.-S. Chin, T.-T. Shun, C.-H. Tsau, S.-Y. Chang, Advanced Engineering Materials 6, 299–303 (2004)
Y. Zhang, Y.J. Zhou, J.P. Lin, G.L. Chen, P.K. Liaw, Advanced Engineering Materials 10, 534–538 (2008)
S. Guo, C. Ng, J. Lu, C. Liu, Journal of applied physics 109, 103505 (2011)
G. Sheng, C.T. Liu, Progress in Natural Science: Materials International 21, 433–446 (2011)
X. Yang, Y. Zhang, Materials Chemistry and Physics 132, 233–238 (2012)
H. Bhadeshia, R. Dimitriu, S. Forsik, J. Pak, J. Ryu, Materials Science and Technology 25, 504–510 (2009)
J. Schmidt and M. Marques, npj Computational Materials, 2019, 5, 1–36
W. Huang, P. Martin, H.L. Zhuang, Acta Materialia 169, 225–236 (2019)
L. Nastac, Modeling and Simulation of Microstructure Evolution in Solidifying Alloys (Springer, US, 2004).
J.M. Boileau, J.W. Zindel, J.E. Allison, SAE Trans. 106, 63–74 (1997)
J. Lee, H. Kim, C. Won, B. Cantor, Materials Science and Engineering: A 338, 182–190 (2002)
E. Hajjari, M. Divandari, Materials & Design 29, 1685–1689 (2008)
L. Zhang, Y. Jiang, Z. Ma, S. Shan, Y. Jia, C. Fan, W. Wang, Journal of materials processing technology 207, 107–111 (2008)
M. Shabani, A. Mazahery, Archives of Metallurgy and Materials 56, 671–675 (2011)
M.O. Shabani, A. Mazahery, JOM 63, 132 (2011)
S. Boontein, N. Srisukhumbovornchai, J. Kajornchaiyakul, C. Limmaneevichitr, International Journal of Cast Metals Research 24, 108–112 (2011)
J. Jeon, D. Bae, Journal of Alloys and Compounds 808, 151756 (2019)
T. Sivarupan, C.H. Caceres, J.A. Taylor, Metallurgical and Materials Transactions A 44, 4071–4080 (2013)
V. Ront, A. RolSz, International Journal of Cast Metals Research 13, 337–342 (2001)
B. Dutta, M. Rettenmayr, Materials Science and Engineering: A 283, 218–224 (2000)
W.R. Osorio, P.R. Goulart, A. Garcia, G.A. Santos, C.M. Neto, Metallurgical and Materials Transactions A 37, 2525–2538 (2006)
M. Ghoncheh, S. Shabestari, M. Abbasi, Journal of Thermal Analysis and Calorimetry 117, 1253–1261 (2014)
G. Zhao, C. Ding, M. Gu, International Journal of Cast Metals Research 32, 36–45 (2019)
S. Shabestari, M. Malekan, Canadian metallurgical quarterly 44, 305–312 (2005)
E. Ghassemali, M. Riestra, T. Bogdanoff, B.S. Kumar, S. Seifeddine, Procedia Engineering 207, 19–24 (2017)
M. Paliwal, I.-H. Jung, Acta materialia 61, 4848–4860 (2013)
M. Peres, C. Siqueira, A. Garcia, Journal of Alloys and Compounds 381, 168–181 (2004)
L.G. Gomes, D.J. Moutinho, I.L. Ferreira, O.L. Rocha, A. Garci: Appl. Mech. Mater. 2015, vol. 709, pp. 102–5
S. Seifeddine, S. Johansson, I.L. Svensson, Materials Science and Engineering: A 490, 385–390 (2008)
H. **aowu, A. Fanrong, Y. Hong, Acta Metallurgica Sinica (English Letters) 25, 272–278 (2012)
A. Samuel, F. Samuel, Journal of Materials Science 30, 1698–1708 (1995)
K.S. Cruz, E.S. Meza, F.A. Fernandes, J.M. Quaresma, L.C. Casteletti, A. Garcia, Metallurgical and Materials Transactions A 41, 972–984 (2010)
R. Ahmad, M. Asmael, N. Shahizan, S. Gandouz, International Journal of Minerals. Metallurgy, and Materials 24, 91–101 (2017)
A.M. Mullis, L. Farrell, R.F. Cochrane, N.J. Adkins, Metallurgical and materials Transactions B 44, 992–999 (2013)
H. Jones, Journal of Materials Science 19, 1043–1076 (1984)
A. Dong and L. Nastac, IOP Conference Series: Materials Science and Engineering, 2020, p. 012069
A. Schneider, G. Hommel, M. Blettner, Deutsches Ärzteblatt International 107, 776 (2010)
Y. Sakamoto, M. Ishiguro, G. Kitagawa, Dordrecht, The Netherlands: D. Reidel 81, 1–2 (1986)
R.E. Kass, L. Wasserman, J ournal of the american statistical association 90, 928–934 (1995)
N.J. Nagelkerke et al., Biometrika 78, 691–692 (1991)
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Manuscript submitted January 25, 2021; accepted April 8, 2021.
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Dong, A., Nastac, L. Prediction of Secondary Dendrite Arm Spacing in Al Alloys Using Machine Learning. Metall Mater Trans B 52, 2395–2403 (2021). https://doi.org/10.1007/s11663-021-02183-w
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DOI: https://doi.org/10.1007/s11663-021-02183-w