Cu-Ni-Si Alloys Properties Dataset

Published: 13 March 2024| Version 1 | DOI: 10.17632/7tdd6vngzm.1
Mihail Kolev


This comprehensive dataset is specifically designed for the exploration of mechanical properties and electrical conductivity in Cu-Ni-Si alloys, offering detailed insights into chemical compositions, thermo-mechanical processing variables, and their impacts on alloy properties. The collection provides an extensive foundation for understanding and analyzing how various factors influence the performance and characteristics of Cu-Ni-Si alloys. The dataset was curated to facilitate the development and validation of a predictive Hybrid Deep Learning (DL) and Ensemble Learning (EL) model that aims to fill the research gaps in the current understanding of Cu-Ni-Si alloys. It includes data on alloy compositions, processing conditions, and the resultant electrical and mechanical characteristics. The unique combination of DL and EL techniques provides a robust framework for predicting alloy behavior, which is demonstrated through superior predictive performance, showcased by near-perfect R2 values for both training and test datasets. Moreover, for those looking to incorporate machine learning techniques into materials science, this dataset provides a unique opportunity to delve into the complex interplay between alloy composition, processing, and resultant properties. By offering a granular look at these relationships, the dataset opens up new avenues for innovation and research in material science and engineering. The file "Cu-Ni-Si-alloys.xlsx" contains a detailed dataset on various properties of copper-nickel-silicon (Cu-Ni-Si) alloys. It includes columns for the composition of these alloys in terms of percentages of copper (Cu), aluminum (Al), cobalt (Co), chromium (Cr), magnesium (Mg), nickel (Ni), silicon (Si), tin (Sn), and zinc (Zn). Additionally, it provides data on their solid solution strengthening temperature (Tss in K), aging temperature and time, as well as their mechanical and electrical properties such as hardness (HV), yield strength (MPa), ultimate tensile strength (MPa), and electrical conductivity (%IACS). Each entry also includes a DOI link to its source and references for further reading. The dataset presented herein is extracted from the comprehensive collection of data on the mechanical properties and electrical conductivity of copper-based alloys curated by Gorsse, Stephane; Gouné, Mohamed; LIN, Wei-Chih; Girard, Lionel (2023) titled "Dataset of mechanical properties and electrical conductivity of copper-based alloys" available on figshare (Collection, DOI:



Institut po metaloznanie saorazenija i tehnologii s tsentar po hidro- i aerodinamika Akademik Angel Balevski Balgarska akademija na naukite


Materials Science, Copper Alloys


Bulgarian National Science Fund