Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 11 |
| Fachzeitschrift | Production Engineering |
| Frühes Online-Datum | 3 Dez. 2024 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 3 Dez. 2024 |
Abstract
Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Production Engineering, 03.12.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Examination of inline process monitoring of the cross-wedge rolling process using AI-based image recognition
AU - Merkel, Paulina
AU - Doede, Nils
AU - Kriwall, Mareile
AU - Stonis, Malte
AU - Behrens, Bernd Arno
N1 - Publisher Copyright: © The Author(s) under exclusive licence to German Academic Society for Production Engineering (WGP) 2024.
PY - 2024/12/3
Y1 - 2024/12/3
N2 - Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.
AB - Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.
KW - AI-based image recognition
KW - Cross-wedge rolling
KW - Hybrid components
KW - Process monitoring
UR - http://www.scopus.com/inward/record.url?scp=85211823861&partnerID=8YFLogxK
U2 - 10.1007/s11740-024-01321-y
DO - 10.1007/s11740-024-01321-y
M3 - Article
AN - SCOPUS:85211823861
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
ER -