A digital twin framework for prediction, monitoring, and diagnosis in copper tube extrusion
Kyriakos SABATAKAKIS, Panagiotis STAVROPOULOS, Apostolos KAIMENOPOULOS, Dimitrios KARATASIOS
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.
Keywords
Metal Forming, Copper Extrusion, Digital Twins, Industry 4.0
Published online 12/10/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Kyriakos SABATAKAKIS, Panagiotis STAVROPOULOS, Apostolos KAIMENOPOULOS, Dimitrios KARATASIOS, A digital twin framework for prediction, monitoring, and diagnosis in copper tube extrusion, Materials Research Proceedings, Vol. 46, pp 211-218, 2024
DOI: https://doi.org/10.21741/9781644903377-28
The article was published as article 28 of the book Innovative Manufacturing Engineering and Energy
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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