Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation

DOI10.1016/j.cma.2022.114652OpenAlexW4211198540MaRDI QIDQ2138688

Wentao Yan, Fan Chen, Min Yang

Publication date: 12 May 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2022.114652

zbMATH Keywords

grain growththermal stresstemperature fieldadditive manufacturingthermal-fluid flowdata-driven modeling

Mathematics Subject Classification ID

Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)

Related Items (1)

An efficient and high-fidelity local multi-mesh finite volume method for heat transfer and fluid flow problems in metal additive manufacturing

Uses Software

• PINN

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support
• Volume of fluid (VOF) method for the dynamics of free boundaries
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
• Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach
• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

This page was built for publication: Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation