Details
| Original language | English |
|---|---|
| Title of host publication | Advances in System-Integrated Intelligence - Proceedings of the 6th International Conference on System-Integrated Intelligence SysInt 2022, Genova, Italy |
| Editors | Maurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben |
| Place of Publication | Cham |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 23-32 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-031-16281-7 |
| ISBN (print) | 9783031162800 |
| Publication status | Published - 2023 |
| Event | 6th International Conference on System-Integrated Intelligence, SysInt 2022 - Genova, Italy Duration: 7 Sept 2022 → 9 Sept 2022 |
Publication series
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Volume | 546 LNNS |
| ISSN (Print) | 2367-3370 |
| ISSN (electronic) | 2367-3389 |
Abstract
During milling, process forces are acting on the cutting tool, causing tool deflection and subsequently a shape deviation of the workpiece. To compensate these effects, knowledge of the process forces is required. In this work, machine learning (ML) methods are applied to reconstruct process forces from the drive signals of two different milling centers. The results of a linear regression, bagged trees and a stacked LSTM are presented. The approaches show different results depending on the milling center. Only for the LSTM an error lower than 30 N is achieved for both machine tools. Independent of the ML approach, the results strongly depend on the selection of milling processes used for training.
Keywords
- Artificial neural network, Machine tool, Machine-learning, Milling
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Computer Networks and Communications
Cite this
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Advances in System-Integrated Intelligence - Proceedings of the 6th International Conference on System-Integrated Intelligence SysInt 2022, Genova, Italy. ed. / Maurizio Valle; Dirk Lehmhus; Christian Gianoglio; Edoardo Ragusa; Lucia Seminara; Stefan Bosse; Ali Ibrahim; Klaus-Dieter Thoben. Cham: Springer Science and Business Media Deutschland GmbH, 2023. p. 23-32 (Lecture Notes in Networks and Systems; Vol. 546 LNNS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Machine Learning Based Reconstruction of Process Forces
AU - Denkena, Berend
AU - Klemme, Heinrich
AU - Stoppel, Dennis
N1 - Funding Information: We thank the “Sieglinde Vollmer Stiftung” for funding this research.
PY - 2023
Y1 - 2023
N2 - During milling, process forces are acting on the cutting tool, causing tool deflection and subsequently a shape deviation of the workpiece. To compensate these effects, knowledge of the process forces is required. In this work, machine learning (ML) methods are applied to reconstruct process forces from the drive signals of two different milling centers. The results of a linear regression, bagged trees and a stacked LSTM are presented. The approaches show different results depending on the milling center. Only for the LSTM an error lower than 30 N is achieved for both machine tools. Independent of the ML approach, the results strongly depend on the selection of milling processes used for training.
AB - During milling, process forces are acting on the cutting tool, causing tool deflection and subsequently a shape deviation of the workpiece. To compensate these effects, knowledge of the process forces is required. In this work, machine learning (ML) methods are applied to reconstruct process forces from the drive signals of two different milling centers. The results of a linear regression, bagged trees and a stacked LSTM are presented. The approaches show different results depending on the milling center. Only for the LSTM an error lower than 30 N is achieved for both machine tools. Independent of the ML approach, the results strongly depend on the selection of milling processes used for training.
KW - Artificial neural network
KW - Machine tool
KW - Machine-learning
KW - Milling
UR - http://www.scopus.com/inward/record.url?scp=85137996229&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16281-7_3
DO - 10.1007/978-3-031-16281-7_3
M3 - Conference contribution
AN - SCOPUS:85137996229
SN - 9783031162800
T3 - Lecture Notes in Networks and Systems
SP - 23
EP - 32
BT - Advances in System-Integrated Intelligence - Proceedings of the 6th International Conference on System-Integrated Intelligence SysInt 2022, Genova, Italy
A2 - Valle, Maurizio
A2 - Lehmhus, Dirk
A2 - Gianoglio, Christian
A2 - Ragusa, Edoardo
A2 - Seminara, Lucia
A2 - Bosse, Stefan
A2 - Ibrahim, Ali
A2 - Thoben, Klaus-Dieter
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 6th International Conference on System-Integrated Intelligence, SysInt 2022
Y2 - 7 September 2022 through 9 September 2022
ER -