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...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

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

Similar Items