Machine learning application for wear rate prediction of WC/Co-based cermet with different content of Ni, Cr, TiC, TaC, and NbC

Wear rate of WC/Co-based cermet materials under severe tribological conditions is a critical thermomechanical property that can limit the practical application of various tools and industrial machinery. In this paper, three machine learning (ML) algorithms including Random Forest, Gradient Boosting...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2024-12, Vol.135 (11), p.5945-5959
Main Authors: Harouz, Riad, Zelmatı, Djamel, Khelil, Khaled
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Cermets
Coefficient of friction
Computer-Aided Engineering (CAD
Data points
Dry friction
Engineering
High temperature
Impact analysis
Industrial and Production Engineering
Machine learning
Mechanical Engineering
Mechanical properties
Media Management
Niobium carbide
Original Article
Performance assessment
Physical properties
Powder metallurgy
Thermomechanical properties
Tribology
Tungsten carbide
Wear rate
Citations: Items that this one cites
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Summary:Wear rate of WC/Co-based cermet materials under severe tribological conditions is a critical thermomechanical property that can limit the practical application of various tools and industrial machinery. In this paper, three machine learning (ML) algorithms including Random Forest, Gradient Boosting Regressor, and XGBoost are employed to predict the wear rate of WC/Co-based cermets elaborated through powder metallurgy, utilizing dry friction under severe pin-on-disk conditions at elevated temperatures. The study analyzes 116 experimental tribological data points to assess the impact of sliding speed, additive content, hardness, friction coefficient, density, and temperature on estimating the wear of various cermet samples, all tested under a constant normal load of 20 N. The performance assessment shows that ML-based models could effectively predict the wear rate, with the Random Forest algorithm outperforming the others, achieving a coefficient of determination ( R 2 ) of 0.8438. Additionally, a comparative analysis is conducted to assess the performance of the ML-based models relative to one another. The models successfully predicted the wear rate of WC/Co-based cermets across various grades, tribological parameters, and physical and mechanical properties, achieving satisfactory accuracy.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-14862-4