Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures

A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a mater...

Descripción completa

Detalles Bibliográficos
Autores principales: Cheloee Darabi, Ali, Rastgordani, Shima, Khoshbin, Mohammadreza, Guski, Vinzenz, Schmauder, Siegfried
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822330/
https://www.ncbi.nlm.nih.gov/pubmed/36614791
http://dx.doi.org/10.3390/ma16010447

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822330/
https://www.ncbi.nlm.nih.gov/pubmed/36614791
http://dx.doi.org/10.3390/ma16010447

Ejemplares similares