A digital twin framework for prediction, monitoring, and diagnosis in copper tube extrusion

Abstract. The strict quality requirements, the increased production rates, variability, and volume constitute challenges in the quality of extruded profiles. State-of-practice methods cannot track down or mitigate all the product features and defects, thus increasing scrap generation and reducing production flexibility. Nevertheless, State-of-the-art approaches are few and dedicated to the product rather than the actual process with the last being researched in terms of process modeling. This study assesses the adoption level of I4.0 technologies in extrusion applications and it applies well-established practices across manufacturing to outline a Digital Twin framework for Prediction, Monitoring, and Diagnosis in the case of copper tube extrusion.

Authors

Institutions

Publication Details

Journal
Materials research proceedings
Published
2024-12-04
DOI
https://doi.org/10.21741/9781644903377-28
Citations
1
Primary Topic
Metallurgy and Material Forming
Type
article
Field-Weighted Citation Impact
0.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A digital twin framework for prediction, monitoring, and diagnosis in copper tube extrusion

Kyriakos SABATAKAKIS
1 citations
Materials research proceedings
Metallurgy and Material Forming
0.27
article

A digital twin framework for prediction, monitoring, and diagnosis in copper tube extrusion

Kyriakos SABATAKAKIS
article en
1 citations

Abstract

Abstract. The strict quality requirements, the increased production rates, variability, and volume constitute challenges in the quality of extruded profiles. State-of-practice methods cannot track down or mitigate all the product features and defects, thus increasing scrap generation and reducing production flexibility. Nevertheless, State-of-the-art approaches are few and dedicated to the product rather than the actual process with the last being researched in terms of process modeling. This study assesses the adoption level of I4.0 technologies in extrusion applications and it applies well-established practices across manufacturing to outline a Digital Twin framework for Prediction, Monitoring, and Diagnosis in the case of copper tube extrusion.

Materials research proceedingsVol. 46
University of Patras (GR)
European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 47%
Metallurgy and Material Forming
0.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.