Physics-informed machine learning-based real-time long-horizon temperature fields prediction in metallic additive manufacturing

Guardado en:

Detalles Bibliográficos
Publicado en:	Communications Engineering vol. 4, no. 1 (Dec 2025), p. 168
Autor principal:	Tian, Mingxuan
Otros Autores:	Mu, Haochen, Liu, Tao, Li, Mengjiao, Ding, Donghong, Zhao, Jianping
Publicado:	Springer Nature B.V.
Materias:	Temperature distribution Finite element method Finite volume method Accuracy Fluid dynamics Parameter identification Quality assurance Boundary conditions Data processing Manufacturing Machine learning Low carbon steel Additive manufacturing Efficiency Simulation Physics Partial differential equations Residual stress Temperature Neural networks Process controls Recurrent neural networks Data collection Finite element analysis Errors Real time
Acceso en línea:	Citation/Abstract Full Text Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Sea el primero en dejar un comentario!