Details
| Original language | English |
|---|---|
| Article number | 282 |
| Number of pages | 13 |
| Journal | Machines |
| Volume | 9 |
| Issue number | 11 |
| Publication status | Published - 10 Nov 2021 |
Abstract
Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face‐turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density‐based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.
Keywords
- Knowledge transfer, Machine tools, Process monitoring, Turning
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science (miscellaneous)
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Industrial and Manufacturing Engineering
- Engineering(all)
- Electrical and Electronic Engineering
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In: Machines, Vol. 9, No. 11, 282, 10.11.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Transfer of Process References between Machine Tools for Online Tool Condition Monitoring
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Stiehl, Tobias H.
N1 - Funding Information: Funding: The authors acknowledge financial support by the Federal Ministry for Economic Affairs and Energy of Germany (BMWi) in the project IIP‐Ecosphere (project number 01MK20006A).
PY - 2021/11/10
Y1 - 2021/11/10
N2 - Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face‐turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density‐based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.
AB - Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face‐turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density‐based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.
KW - Knowledge transfer
KW - Machine tools
KW - Process monitoring
KW - Turning
UR - http://www.scopus.com/inward/record.url?scp=85119069309&partnerID=8YFLogxK
U2 - 10.3390/machines9110282
DO - 10.3390/machines9110282
M3 - Article
AN - SCOPUS:85119069309
VL - 9
JO - Machines
JF - Machines
IS - 11
M1 - 282
ER -