Machine learning-driven predictive modeling of mechanical properties in diverse steels

This study explores the application of machine learning (ML) in steel design using a small dataset of various steel grades that include 13 key elements and three critical mechanical properties. Random forest (RF) models were systematically evaluated for their robustness and effectiveness in predicti...

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Bibliographic Details
Main Authors: Movaffaq Kateb, Sahar Safarian
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Machine Learning with Applications
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827025000179
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