Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys

Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys

This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid acti...

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Bibliographic Details
Main Authors: Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Crystals
Subjects:
machine learning algorithms
backpropagation
artificial neural networks
high-entropy alloys
hardness
Online Access:https://www.mdpi.com/2073-4352/15/5/404
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