5 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study
Yifan Tang, M. Rahmani Dehaghani, G. Gary Wang
Transfer learning (TL) based additive manufacturing (AM) modeling is an emerging field to reuse the data from historical products and mitigate the data insufficiency in modeling ne…