Investigating the impact of nano copper on the electrical conductivity of aluminum: A comprehensive study utilizing ANN, genetic algorithm, and fuzzy logic methods
University of California. Santa Cruz, Silicon Valley Extension, Santa Clara, CA.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 954–958.
Article DOI: 10.30574/wjaets.2024.13.1.0471
Publication history:
Received on 22 August 2024; revised on 07 October 2024; accepted on 10 October 2024
Abstract:
Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, and 100°C). Additionally, we compare the predictive capabilities of Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Fuzzy Logic (FL) methods in forecasting the electrical conductivity variations of Al based on these parameters.
Keywords:
Electrical conductivity; GA method; ANN; Fuzzy Logic; Predictive model; Nano particle
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0