Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 49-54 |
| Seitenumfang | 6 |
| Fachzeitschrift | CIRP Journal of Manufacturing Science and Technology |
| Jahrgang | 24 |
| Frühes Online-Datum | 7 Dez. 2018 |
| Publikationsstatus | Veröffentlicht - Jan. 2019 |
Abstract
This paper presents a self-optimizing process planning approach for 5-axis milling that allows an automatic compensation for tool deflection. For this purpose, process conditions are obtained from a process-parallel material removal simulation and merged with shape error measurements. Using machine learning methods, the resulting shape error is predicted and the tool path adapted automatically. The system has been implemented on a 5-axis CNC machine centre. It is shown that the resulting shape error can be reduced by 50%. Moreover, the article highlights the behaviour of the learning process and the transferability to other workpiece geometries.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: CIRP Journal of Manufacturing Science and Technology, Jahrgang 24, 01.2019, S. 49-54.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Self-optimizing tool path generation for 5-axis machining processes
AU - Dittrich, Marc-André
AU - Uhlich, Florian
AU - Denkena, Berend
N1 - Funding information: The presented investigations were conducted within the Collaborative Research Centre 653 project K2. We thank the German Research Foundation for the support of this project.
PY - 2019/1
Y1 - 2019/1
N2 - This paper presents a self-optimizing process planning approach for 5-axis milling that allows an automatic compensation for tool deflection. For this purpose, process conditions are obtained from a process-parallel material removal simulation and merged with shape error measurements. Using machine learning methods, the resulting shape error is predicted and the tool path adapted automatically. The system has been implemented on a 5-axis CNC machine centre. It is shown that the resulting shape error can be reduced by 50%. Moreover, the article highlights the behaviour of the learning process and the transferability to other workpiece geometries.
AB - This paper presents a self-optimizing process planning approach for 5-axis milling that allows an automatic compensation for tool deflection. For this purpose, process conditions are obtained from a process-parallel material removal simulation and merged with shape error measurements. Using machine learning methods, the resulting shape error is predicted and the tool path adapted automatically. The system has been implemented on a 5-axis CNC machine centre. It is shown that the resulting shape error can be reduced by 50%. Moreover, the article highlights the behaviour of the learning process and the transferability to other workpiece geometries.
KW - Adaptive manufacturing
KW - Compensation
KW - Computer aided manufacturing (CAM)
KW - Machine learning
KW - Milling
KW - Tool path
UR - http://www.scopus.com/inward/record.url?scp=85057755422&partnerID=8YFLogxK
U2 - 10.1016/j.cirpj.2018.11.005
DO - 10.1016/j.cirpj.2018.11.005
M3 - Article
AN - SCOPUS:85057755422
VL - 24
SP - 49
EP - 54
JO - CIRP Journal of Manufacturing Science and Technology
JF - CIRP Journal of Manufacturing Science and Technology
SN - 1755-5817
ER -