Application of Recurrent Neural Networks in Uncertainty Analysis of Sheet Metal Forming

Application of Recurrent Neural Networks in Uncertainty Analysis of Sheet Metal Forming

The quality of deep-drawn sheet metal components can be strongly influenced by different sources of uncertainty, such as variations in process conditions, deviations in tool geometry, and variations in material properties between coils. Identifying the underlying causes of forming defects remains a...

Full description

Saved in:
Bibliographic Details
Main Authors: Cruz Daniel, Parreira Tomás, Marques Armando, Prates Pedro, Oliveira Marta, Neto Diogo, Santos Abel, Amaral Rui, Barbosa Manuel, Pereira André
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:MATEC Web of Conferences
Subjects:
sheet metal forming
uncertainty quantification
machine learning (ml)
recurrent neural networks (rnns)
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2025/02/matecconf_iddrg2025_01071.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items