Long Short-Term Memory to Predict the Quality Parameters in the Hot Rolling Process
p.81
p.81
AI-Based Predictions of Forming Effects for Enhanced Crash Simulation
p.93
p.93
Residual Learning-Based Synthetic Data for Hybrid Metamodeling in the Stamping of an Automotive Door Panel
p.103
p.103
Robust and Feature-Aware Arbritary Lagrangian-Eulerian Method for Material Forming Applications
p.113
p.113
On-Line Prediction of Dry Zones during Composite Process through Digital Shadow Approach
p.123
p.123
Graph Neural Network for Draw-in Prediction in Sheet Metal Stamping
p.137
p.137
A Bayesian Data Assimilation Framework for Characterizing Recrystallization in Steels
p.145
p.145
Predicting Lateral Flow in Hot Sheet Metal Rolling Using Symbolic Regression
p.157
p.157
Physics-Informed Recurrent Neural Networks with Kinematic Constraints for Large Deformation Metal Forming
p.171
p.171
On-Line Prediction of Dry Zones during Composite Process through Digital Shadow Approach
Abstract:
A novel solution for monitoring the infusion process and providing decision support to operators involved in the manufacturing of large, unique or near-unique parts is presented. Based on a scientific approach referred to as the 5D methodology (D for dimensions), the proposed solution consists of a process digital twin built upon a metamodel that is fed in real time by signals from sensors embedded in the process, enabling the anticipation of defects such as dry spots.
You have full access to the following eBook
Info:
Periodical:
Key Engineering Materials (Volume 1050)
Pages:
123-136
DOI:
https://doi.org/10.4028/p-Us6DNo
DOI link
Citation:
Online since:
April 2026
Keywords:
Permissions:
Share:
Citation: