Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 282-287 |
| Seitenumfang | 6 |
| Fachzeitschrift | Procedia CIRP |
| Jahrgang | 88 |
| Frühes Online-Datum | 13 Juni 2020 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2019 - Naples, Italien Dauer: 17 Juli 2019 → 19 Juli 2019 |
Abstract
Chatter is a limiting factor for productivity in milling. Choosing cutting parameters that ensure a stable and productive process is not a trivial task. Stability lobe diagrams (SLD) help to find suitable parameters for machining. This paper examines the suitability of support vector machines (SVM) and artificial neuronal networks (ANN) for this application. In addition, kernel interpolation as a new algorithm for this approach is introduced. The algorithms are tested on simulated as well as on measurement data from a real process. It is shown that ML algorithms are able to learn SLDs during process.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Procedia CIRP, Jahrgang 88, 2020, S. 282-287.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Analysis of different machine learning algorithms to learn stability lobe diagrams
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Reimer, Svenja
PY - 2020
Y1 - 2020
N2 - Chatter is a limiting factor for productivity in milling. Choosing cutting parameters that ensure a stable and productive process is not a trivial task. Stability lobe diagrams (SLD) help to find suitable parameters for machining. This paper examines the suitability of support vector machines (SVM) and artificial neuronal networks (ANN) for this application. In addition, kernel interpolation as a new algorithm for this approach is introduced. The algorithms are tested on simulated as well as on measurement data from a real process. It is shown that ML algorithms are able to learn SLDs during process.
AB - Chatter is a limiting factor for productivity in milling. Choosing cutting parameters that ensure a stable and productive process is not a trivial task. Stability lobe diagrams (SLD) help to find suitable parameters for machining. This paper examines the suitability of support vector machines (SVM) and artificial neuronal networks (ANN) for this application. In addition, kernel interpolation as a new algorithm for this approach is introduced. The algorithms are tested on simulated as well as on measurement data from a real process. It is shown that ML algorithms are able to learn SLDs during process.
KW - Artificial neuronal networks
KW - Kernel interpolation
KW - Machine learning
KW - Stability lobe diagrams
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85089080382&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2020.05.049
DO - 10.1016/j.procir.2020.05.049
M3 - Conference article
AN - SCOPUS:85089080382
VL - 88
SP - 282
EP - 287
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2019
Y2 - 17 July 2019 through 19 July 2019
ER -