7 citations · 12 across the 5 of their papers we have counts for
5 papers
Accident Impact Prediction based on a deep convolutional and recurrent neural network model
Pouyan Sajadi, Mahya Qorbani, Sobhan Moosavi +1
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive…
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing
Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi +1
Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to…
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…