Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments
Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurat...
- Autores:
-
Ortega-Portilla, Carolina
Mambuscay, Claudia Lorena
Piamba Jiménez, Jeferson Fernando
Forero Vargas, Manuel Guillermo
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5915
- Acceso en línea:
- https://doi.org/ 10.3390/ma17102235
https://hdl.handle.net/20.500.12313/5915
https://www.mdpi.com/1996-1944/17/10/2235
- Palabra clave:
- Dureza Vickers - Modelado predictivo
Acero D2 - Aprendizaje automático
Coating
Indentation imprint
Machine learning
Regression
Titanium Niobium Nitride (TiNbN)
Vickers hardness
- Rights
- openAccess
- License
- © 2024 by the authors.
