Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 12 |
| Fachzeitschrift | Production Engineering |
| Frühes Online-Datum | 3 Juli 2024 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 3 Juli 2024 |
Abstract
Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Production Engineering, 03.07.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard
AU - Doede, Nils
AU - Merkel, Paulina
AU - Kriwall, Mareile
AU - Stonis, Malte
AU - Behrens, Bernd Arno
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/7/3
Y1 - 2024/7/3
N2 - Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.
AB - Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.
KW - Hot forming
KW - Predictive maintenance
KW - Process monitoring
KW - Quality management
KW - Wear
UR - http://www.scopus.com/inward/record.url?scp=85197924534&partnerID=8YFLogxK
U2 - 10.1007/s11740-024-01298-8
DO - 10.1007/s11740-024-01298-8
M3 - Article
AN - SCOPUS:85197924534
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
ER -