Data analytics approach to predict the hardness of copper matrix composites

Somesh Kr Bhattacharya, Ryoji Sahara, Dušan Božić, Jovana Ružić

研究成果: Article査読

抄録

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 formula presented), average particle size (PS, μm), density formula presented) and yield stress (formula presented) 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.

本文言語English
ページ(範囲)357-364
ページ数8
ジャーナルMetallurgical and Materials Engineering
26
4
DOI
出版ステータスPublished - 2020
外部発表はい

ASJC Scopus subject areas

  • 機械工学
  • 金属および合金

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