Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 2449-2456 |
| Seitenumfang | 8 |
| Fachzeitschrift | Journal of Intelligent Manufacturing |
| Jahrgang | 30 |
| Ausgabenummer | 6 |
| Frühes Online-Datum | 22 Feb. 2018 |
| Publikationsstatus | Veröffentlicht - 1 Aug. 2019 |
Abstract
New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Informatik (insg.)
- Artificial intelligence
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Journal of Intelligent Manufacturing, Jahrgang 30, Nr. 6, 01.08.2019, S. 2449-2456.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Witt, Matthias
N1 - Funding information: The results presented in this paper were obtained within the Collaborative Research Centre 1153 “Process chain to produce hybrid high performance components by Tailored Forming” in the subproject B5. The authors would like to thank the German Research Foundation (DFG) for the financial and organisational support of this project.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.
AB - New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.
KW - Hybrid parts
KW - Machine-learning
KW - Monitoring
KW - Turning
UR - http://www.scopus.com/inward/record.url?scp=85042375594&partnerID=8YFLogxK
U2 - 10.1007/s10845-018-1404-0
DO - 10.1007/s10845-018-1404-0
M3 - Article
AN - SCOPUS:85042375594
VL - 30
SP - 2449
EP - 2456
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 6
ER -