Details
| Translated title of the contribution | Tool Wear Monitoring Using Process Data of Multiple Machine Tools by Means of Machine Learning |
|---|---|
| Original language | German |
| Pages (from-to) | 298-301 |
| Number of pages | 4 |
| Journal | ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb |
| Volume | 118 |
| Issue number | 5 |
| Publication status | Published - 1 May 2023 |
Abstract
Monitoring the actual wear of a tool enables a tool to be used to the end of its life, despite tool life variations. However, such monitoring currently requires an extensive teach-in on the monitored machine. This article describes an approach for tool wear monitoring that omits the machine-specific teach-in phase. Instead, the teach-in is based on data that was previously recorded on other machines. Further, a demonstrator for monitoring flank wear width during milling is presented.
ASJC Scopus subject areas
- Engineering(all)
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
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In: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Vol. 118, No. 5, 01.05.2023, p. 298-301.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Überwachung von Werkzeugverschleiß Maschinenübergreifende Nutzung von Prozessdaten mithilfe von Maschinellem Lernen
AU - Denkena, Berend
AU - Klemme, Heinrich
AU - Stiehl, Tobias H.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Monitoring the actual wear of a tool enables a tool to be used to the end of its life, despite tool life variations. However, such monitoring currently requires an extensive teach-in on the monitored machine. This article describes an approach for tool wear monitoring that omits the machine-specific teach-in phase. Instead, the teach-in is based on data that was previously recorded on other machines. Further, a demonstrator for monitoring flank wear width during milling is presented.
AB - Monitoring the actual wear of a tool enables a tool to be used to the end of its life, despite tool life variations. However, such monitoring currently requires an extensive teach-in on the monitored machine. This article describes an approach for tool wear monitoring that omits the machine-specific teach-in phase. Instead, the teach-in is based on data that was previously recorded on other machines. Further, a demonstrator for monitoring flank wear width during milling is presented.
KW - Federated Learning
KW - Machine Tools
KW - Milling
KW - Monitoring
KW - Tool Wear
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85159784737&partnerID=8YFLogxK
U2 - 10.1515/zwf-2023-1059
DO - 10.1515/zwf-2023-1059
M3 - Artikel
AN - SCOPUS:85159784737
VL - 118
SP - 298
EP - 301
JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
SN - 0947-0085
IS - 5
ER -