Data analytics approach to predict the hardness of copper matrix composites
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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.
Кључне речи:
Copper Matrix Composites / Hardness / Machine Learning / Regression ModelИзвор:
Metallurgical and Materials Engineering, 2020, 26, 4, 357-364Финансирање / пројекти:
- Council for Science, Technology and Innovation (CSTI), Crossministerial Strategic Innovation Promotion Program (SIP), "Materials Integration for revolutionary design system of structural materials" (Funding agency: JST)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200017 (Универзитет у Београду, Институт за нуклеарне науке Винча, Београд-Винча) (RS-MESTD-inst-2020-200017)
DOI: 10.30544/567
ISSN: 2217-8961
WoS: 000605080200003
Scopus: 2-s2.0-85099483066
Институција/група
VinčaTY - JOUR AU - Bhattacharya, Somesh Kr. AU - Sahara, Ryoji AU - Božić, Dušan AU - Ružić, Jovana PY - 2020 UR - https://vinar.vin.bg.ac.rs/handle/123456789/9524 AB - 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. T2 - Metallurgical and Materials Engineering T1 - Data analytics approach to predict the hardness of copper matrix composites VL - 26 IS - 4 SP - 357 EP - 364 DO - 10.30544/567 ER -
@article{ author = "Bhattacharya, Somesh Kr. and Sahara, Ryoji and Božić, Dušan and Ružić, Jovana", year = "2020", abstract = "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.", journal = "Metallurgical and Materials Engineering", title = "Data analytics approach to predict the hardness of copper matrix composites", volume = "26", number = "4", pages = "357-364", doi = "10.30544/567" }
Bhattacharya, S. Kr., Sahara, R., Božić, D.,& Ružić, J.. (2020). Data analytics approach to predict the hardness of copper matrix composites. in Metallurgical and Materials Engineering, 26(4), 357-364. https://doi.org/10.30544/567
Bhattacharya SK, Sahara R, Božić D, Ružić J. Data analytics approach to predict the hardness of copper matrix composites. in Metallurgical and Materials Engineering. 2020;26(4):357-364. doi:10.30544/567 .
Bhattacharya, Somesh Kr., Sahara, Ryoji, Božić, Dušan, Ružić, Jovana, "Data analytics approach to predict the hardness of copper matrix composites" in Metallurgical and Materials Engineering, 26, no. 4 (2020):357-364, https://doi.org/10.30544/567 . .