Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 341-344 |
| Seitenumfang | 4 |
| Fachzeitschrift | CIRP annals |
| Jahrgang | 70 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 19 Mai 2021 |
| Publikationsstatus | Veröffentlicht - 2021 |
Abstract
On the way to fully autonomous machine tools it is essential to independently select suitable process parameters and adapt them on-the-fly to the appropriate process conditions in a self-controlled manner. Such systems require complex physical process models and are usually limited to feed and spindle speed adaption during the milling process. This paper introduces a new approach enabling machines during the milling process to learn which parameters lead to a stable process with maximum productivity and to adjust them autonomously. It is shown that this approach enables the machine tool to independently find stable process parameters with maximum productivity.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: CIRP annals, Jahrgang 70, Nr. 1, 2021, S. 341-344.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Online adaption of milling parameters for a stable and productive process
AU - Bergmann, Benjamin
AU - Reimer, Svenja
N1 - Funding Information: The authors thank the German Research Foundation (DFG) for its financial and organizational support of the project DE 447/156-1.
PY - 2021
Y1 - 2021
N2 - On the way to fully autonomous machine tools it is essential to independently select suitable process parameters and adapt them on-the-fly to the appropriate process conditions in a self-controlled manner. Such systems require complex physical process models and are usually limited to feed and spindle speed adaption during the milling process. This paper introduces a new approach enabling machines during the milling process to learn which parameters lead to a stable process with maximum productivity and to adjust them autonomously. It is shown that this approach enables the machine tool to independently find stable process parameters with maximum productivity.
AB - On the way to fully autonomous machine tools it is essential to independently select suitable process parameters and adapt them on-the-fly to the appropriate process conditions in a self-controlled manner. Such systems require complex physical process models and are usually limited to feed and spindle speed adaption during the milling process. This paper introduces a new approach enabling machines during the milling process to learn which parameters lead to a stable process with maximum productivity and to adjust them autonomously. It is shown that this approach enables the machine tool to independently find stable process parameters with maximum productivity.
KW - Chatter
KW - Machine Learning
KW - Machine tool
UR - http://www.scopus.com/inward/record.url?scp=85106384880&partnerID=8YFLogxK
U2 - 10.1016/j.cirp.2021.04.086
DO - 10.1016/j.cirp.2021.04.086
M3 - Article
AN - SCOPUS:85106384880
VL - 70
SP - 341
EP - 344
JO - CIRP annals
JF - CIRP annals
SN - 0007-8506
IS - 1
ER -