activity
20232026
most citedReal-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks

5 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints

Ke Wang, Nguyen Gia Hien Vu, Yifan Tang +2

This paper presents a RePaint-enhanced framework that integrates a pre-trained performance-guided denoising diffusion probabilistic model (DDPM) for performance- and parameter-cons…

cs.CE2025

Capturing Lifecycle System Degradation in Digital Twin Model Updating

Yifan Tang, Mostafa Rahmani Dehaghani, G. Gary Wang

Digital twin (DT) has emerged as a powerful tool to facilitate monitoring, control, and other decision-making tasks in real-world engineering systems. Online update methods have be…

cs.LG20245 cited

Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks

Pouyan Sajadi, Mostafa Rahmani Dehaghani, Yifan Tang +1

Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process…

cs.LG2023

Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls

Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi +6

This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity…

eess.SY2023

System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme

M. Rahmani Dehaghani, Atieh Sahraeidolatkhaneh, Morgan Nilsen +4

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rat…