Details
| Original language | English |
|---|---|
| Journal | Advanced engineering materials |
| Early online date | 25 Oct 2024 |
| Publication status | E-pub ahead of print - 25 Oct 2024 |
Abstract
The extrusion process is one of the most important methods for continuous processing of rubber compounds. An extruder is used to give the rubber compound a geometrically defined shape as an extrudate. To ensure that product-specific requirements are fulfilled, the extrusion process and the resulting extrudate are currently monitored using various sensor technologies. Nevertheless, a certain amount of scrap material is produced during the extrusion process, often as a result of unstable process conditions. In this context, one solution for enhancing resource efficiency is the digitalization of the production chain. The aim of this work is to demonstrate an approach for the digitalization of an extrusion line that combines the use of innovative measuring methods for process monitoring and algorithms from the field of artificial intelligence (AI) for process control. For the validation of the individual measuring systems and the process control, various production scenarios in the extrudate production are considered. The results show that the measurement systems for process and extrudate monitoring can directly detect changes in the extrusion process and extrudate quality. Furthermore, the generated data can be used to automatically adjust the extrusion process by the developed AI-based control system.
Keywords
- AI-based process control, digitalization, Laser-induced breakdown spectroscopy, optical metrology, rubber extrusion
ASJC Scopus subject areas
- Materials Science(all)
- General Materials Science
- Physics and Astronomy(all)
- Condensed Matter Physics
Sustainable Development Goals
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In: Advanced engineering materials, 25.10.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Smart Rubber Extrusion Line Combining Multiple Sensor Techniques for AI-Based Process Control
AU - Aschemann, Alexander
AU - Hagen, Paul Felix
AU - Albers, Simon
AU - Rofallski, Robin
AU - Schwabe, Sven
AU - Dagher, Mohammed
AU - Lukas, Marco
AU - Leineweber, Sebastian
AU - Klie, Benjamin
AU - Schneider, Patrick
AU - Bossemeyer, Hagen
AU - Hinz, Lennart
AU - Kästner, Markus
AU - Reitz, Birger
AU - Reithmeier, Eduard
AU - Luhmann, Thomas
AU - Wackerbarth, Hainer
AU - Overmeyer, Ludger
AU - Giese, Ulrich
N1 - Publisher Copyright: © 2024 The Author(s). Advanced Engineering Materials published by Wiley-VCH GmbH.
PY - 2024/10/25
Y1 - 2024/10/25
N2 - The extrusion process is one of the most important methods for continuous processing of rubber compounds. An extruder is used to give the rubber compound a geometrically defined shape as an extrudate. To ensure that product-specific requirements are fulfilled, the extrusion process and the resulting extrudate are currently monitored using various sensor technologies. Nevertheless, a certain amount of scrap material is produced during the extrusion process, often as a result of unstable process conditions. In this context, one solution for enhancing resource efficiency is the digitalization of the production chain. The aim of this work is to demonstrate an approach for the digitalization of an extrusion line that combines the use of innovative measuring methods for process monitoring and algorithms from the field of artificial intelligence (AI) for process control. For the validation of the individual measuring systems and the process control, various production scenarios in the extrudate production are considered. The results show that the measurement systems for process and extrudate monitoring can directly detect changes in the extrusion process and extrudate quality. Furthermore, the generated data can be used to automatically adjust the extrusion process by the developed AI-based control system.
AB - The extrusion process is one of the most important methods for continuous processing of rubber compounds. An extruder is used to give the rubber compound a geometrically defined shape as an extrudate. To ensure that product-specific requirements are fulfilled, the extrusion process and the resulting extrudate are currently monitored using various sensor technologies. Nevertheless, a certain amount of scrap material is produced during the extrusion process, often as a result of unstable process conditions. In this context, one solution for enhancing resource efficiency is the digitalization of the production chain. The aim of this work is to demonstrate an approach for the digitalization of an extrusion line that combines the use of innovative measuring methods for process monitoring and algorithms from the field of artificial intelligence (AI) for process control. For the validation of the individual measuring systems and the process control, various production scenarios in the extrudate production are considered. The results show that the measurement systems for process and extrudate monitoring can directly detect changes in the extrusion process and extrudate quality. Furthermore, the generated data can be used to automatically adjust the extrusion process by the developed AI-based control system.
KW - AI-based process control
KW - digitalization
KW - Laser-induced breakdown spectroscopy
KW - optical metrology
KW - rubber extrusion
UR - http://www.scopus.com/inward/record.url?scp=85207684741&partnerID=8YFLogxK
U2 - 10.1002/adem.202401316
DO - 10.1002/adem.202401316
M3 - Article
AN - SCOPUS:85207684741
JO - Advanced engineering materials
JF - Advanced engineering materials
SN - 1438-1656
ER -