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

Zapisane w:

Opis bibliograficzny
Wydane w:	Communications Engineering vol. 4, no. 1 (Dec 2025), p. 168
1. autor:	Tian, Mingxuan
Kolejni autorzy:	Mu, Haochen, Liu, Tao, Li, Mengjiao, Ding, Donghong, Zhao, Jianping
Wydane:	Springer Nature B.V.
Hasła przedmiotowe:	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
Dostęp online:	Citation/Abstract Full Text Full Text - PDF
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!

Napisz pierwszy komentarz!