1. Breaking hardness and electrical conductivity trade-off in Cu-Ti alloys through machine learning and Pareto front.
- Author
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Fu, Hang, Gao, Tianchuang, Gao, Jianbao, Li, Qin, Meng, Xiangpeng, Zhang, Min, Ling, Haoyue, Zhong, Jing, and Zhang, Lijun
- Subjects
ELECTRIC conductivity ,MACHINE learning ,COPPER-titanium alloys ,COPPER alloys ,ALLOYS ,HARDNESS - Abstract
Balancing the hardness and electrical conductivity of copper alloys within complex compositions and processes poses a formidable challenge. This study proposes a strategy combining machine learning with the Pareto front techniques to identify optimal combinations of composition and processing for Cu-xTi (1.5 ≤ x ≤ 5.4, in wt.%) alloys. Through thermodynamic calculations, precipitation simulations, and experimental characterizations, the microstructural evolution of β'-Cu4Ti precipitates in the designed alloys was explored. The interpretability and predictability of the machine learning model played a crucial role in understanding impact of complex alloy compositions and processing on the evolution of properties, thereby guiding the design of Cu-Ti alloys towards improved attributes. A novel strategy integrating machine learning with Pareto front is proposed for alloy design, facilitating the rapid screening of Cu-Ti alloys that exhibit superior hardness and electrical conductivity simultaneously. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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