Data analytics approach to predict the hardness of copper matrix composites
Copper matrix composite materials have exhibited a high potential in applications where excellent conductivity and mechanical properties are required. In this study, the machine learning models have been applied to predict the hardness of the copper matrix composite materials (CuMCs) produced via the powder metallurgy technique. Six different machine learning regression models were employed. The observed CuMCs were reinforced with two different volume fractions (2 vol.% and 7vol.%) of ZrB2 particles. Based on experimental work, we extracted the independent variables (features) like the milling time (MT, Hours), dislocation density (DD, m-2), average particle size (PS, μm), density (ρ, g/cm3), and yield stress (σ, MPa) while the Vickers hardness (MPa) was used as the dependent variable. Feature selection was performed by calculation the Pearson correlation coefficient (PCC) between the independent and dependent variables. The predictive accuracy higher than 80% was achieved for Cu-7vol.% ZrB2 and lower for the Cu-2vol.% ZrB2.
K. U. Kainer, Metal matrix composites custom-made materials for automotive and aerospace engineering, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2006
S. R. Pogson, P. Fox, C. Sutclisse, W. O'Neill: PRB 9 (2003) 334343.
M. Li, S. J. Zinkle, Comprehensive nuclear materials 4 (2012) 667-690.
Y. Zhan, Y. Z. J. Zeng: Tribol Lett 20 (2005) 163170.
M. Sobhani, A. Mirhabibi, H. Arabi, R. M. D. Brydson: Mater Sci Eng A 577 (2013) 1622.
W. S. Miller, F. J. Humphreys: Scripta Metallurgica et, 25 (1991) 33-38.
C. Zou, Z. Chen, E. Guo, H. Kang, G. Fan, W. Wang: RSC Adv, 8 (2018) 30777-30782.
S. J. Dong, Y. Zhou, Y. Shi, B. Chang: Metall Mater Trans A, 33(4) (2002) 1275-1280.
J. R. Groza, J. C. Gibeling: Mat Sci Eng A-Struct, 171 (1-2) (1993) 115-125.
H. Kimura, N.Muramatsu, K.Suzuki, Copper alloy and copper alloy manufacturing method, Patent Application Publication, US 0211346A1, United States.
X. Fan, X. Huang, Q. Liu, H. Ding, H. Wang, C. Hao: Results Phys, 14 (2019) 102494, 1-6.
J. Ruzic, J. Stasic, S. Markovic, K. Raic, D. Bozic: Sci Sinter, 46 (2) (2014) 217-224.
D. Xue, D. Xue, R. Yuan, Y. Zhou, P. V. Balachandran, X. Ding, J. Sun, T. Lookman: Acta Materialia 125 (2017) 532 - 541.
D. Shin, Y. Yamamoto, M. Brady, S. Lee, J. Haynes, Modern data analytics approach to predict creep of high temperature alloys, Acta Mater, 168 (2019) 321-330.
J. Wei, X. Chu, X.-Y. Sun, K. Xu, H.-X. Deng, J. Chen, Z. Wei, M. Lei: InfoMat 1 (3) (2019) 338-358.
J. Schmidt, M. R. G. Marques, S. Botti, M. A. L. Marques: NPJ Comput Mater, 5 (2019) 83, 1-36.
J. Ruzic, J. Stasic, V. Rajkovic, D. Bozic: Mater and Des, 62 (2014) 409-415.
J. Ruzic, J. Stasic, V. Rajkovic, K. Raic, D. Bozic: Sci Eng Compos Mater, 22 (2015) 665-671.
M. R. Segal, UCSF: Center for Bioinformatics and Molecular Biostatistics, (2004) 1-14.
L. Mason, J. Baxter, P. Bartlett, M. Frean: Advances in neural information processing systems, MIT Press. 12 (1999) 512-518.
H. Drucker, C. C. Burges, L. Kaufman, A. J. Smola, V. N. Vapnik: Advances in neural information processing systems, NIPS, MIT-Press, 9 (1997) 155-161.
V. Cherkassky, Y. Ma: Neural Comput, 15 (2003) 1691-1714.
J. Neter, M. H. Kutner, C. J. Nachtsheim, W. Wasserman, Applied linear statistical models, 4 Edition, Chicago: Irwin, 1996
Y. Zhang, J. Duchi, M. Wainwright: J Mach Learn Res, 16 (2015) 3299-3340.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, G. Louppe, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perror, E. Duchesnay: J Mach Learn Res, 12 (2012) 2825-2830.
C. Sammut, G. I. Webb, Leave-one-out cross-validation in encyclopedia of machine learning, Springer, Boston, MA, 2010.
J. L. Devore, Probability and statistics for engineering and the sciences (8th ed.), Boston, MA: Cengage Learning (2011) 508-510.
Copyright (c) 2020 Somesh Kr. Bhattacharya, Ryoji Sahara, Dušan Božić, Jovana Ruzic
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their published articles online (e.g., in institutional repositories or on their website, social networks like ResearchGate or Academia), as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Except where otherwise noted, the content on this site is licensed under a Creative Commons Attribution 4.0 International License.