Details
| Original language | English |
|---|---|
| Pages (from-to) | 519-524 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 118 |
| Early online date | 18 Jul 2023 |
| Publication status | Published - 2023 |
| Event | 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering 2022 - Naples, Italy Duration: 13 Jul 2022 → 15 Jul 2022 |
Abstract
Monitoring systems may contribute increasing the availability of machine tools and detecting process deviations in time. In the past, machine learning has been used to solve a variety of monitoring problems in machining. However, boundary conditions for the assessment of the principal applicability of machine learning approaches for supervised anomaly detection in machining have not been exhaustively described in the literature. In this paper, objectives as well as deficits of literature approaches are identified and influencing factors on the monitoring quality are described. As a result, we derive boundary conditions and discuss challenges for successful implementation of machine learning based monitoring systems for supervised anomaly detection in industrial practice.
Keywords
- Machine learning, Machining, Monitoring, Quality assurance
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 118, 2023, p. 519-524.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Boundary conditions for the application of machine learning based monitoring systems for supervised anomaly detection in machining
AU - Denkena, B.
AU - Wichmann, M.
AU - Noske, H.
AU - Stoppel, D.
N1 - Funding Information: Funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3489 within the Lower Saxony oab” “V fo e theswoagkV n Foundation and supported by the Center for Digital Innovations (ZDIN).
PY - 2023
Y1 - 2023
N2 - Monitoring systems may contribute increasing the availability of machine tools and detecting process deviations in time. In the past, machine learning has been used to solve a variety of monitoring problems in machining. However, boundary conditions for the assessment of the principal applicability of machine learning approaches for supervised anomaly detection in machining have not been exhaustively described in the literature. In this paper, objectives as well as deficits of literature approaches are identified and influencing factors on the monitoring quality are described. As a result, we derive boundary conditions and discuss challenges for successful implementation of machine learning based monitoring systems for supervised anomaly detection in industrial practice.
AB - Monitoring systems may contribute increasing the availability of machine tools and detecting process deviations in time. In the past, machine learning has been used to solve a variety of monitoring problems in machining. However, boundary conditions for the assessment of the principal applicability of machine learning approaches for supervised anomaly detection in machining have not been exhaustively described in the literature. In this paper, objectives as well as deficits of literature approaches are identified and influencing factors on the monitoring quality are described. As a result, we derive boundary conditions and discuss challenges for successful implementation of machine learning based monitoring systems for supervised anomaly detection in industrial practice.
KW - Machine learning
KW - Machining
KW - Monitoring
KW - Quality assurance
UR - http://www.scopus.com/inward/record.url?scp=85173581308&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2023.06.089
DO - 10.1016/j.procir.2023.06.089
M3 - Conference article
AN - SCOPUS:85173581308
VL - 118
SP - 519
EP - 524
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering 2022
Y2 - 13 July 2022 through 15 July 2022
ER -