Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 117-122 |
| Seitenumfang | 6 |
| Fachzeitschrift | Procedia CIRP |
| Jahrgang | 126 |
| Frühes Online-Datum | 9 Okt. 2024 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023 - Naples, Italien Dauer: 12 Juli 2023 → 14 Juli 2023 |
Abstract
Tool condition monitoring systems are a prerequisite for autonomous production. However, monitoring small series is challenging as systems usually require references of correct processes for parameterization. As an alternative, these references can be sourced from other machine tools that execute similar processes. Unfortunately, actual processes vary among machine tools, despite machine tools using identical NC-instructions. This work introduces a novel multidimensional position-based online monitoring approach that is designed to overcome process variations among machine tools. This approach utilizes dynamic time warping to match the multidimensional tool paths of machine tools. The results include monitoring of pocket milling on three machine tools using process forces sourced from one, or multiple, other machine tools. A comparison demonstrates that position-based limits for online monitoring, rather than time-based limits, are better suited for transfer among machine tools.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Procedia CIRP, Jahrgang 126, 2024, S. 117-122.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Multidimensional position-based monitoring of machining using references of multiple machine tools
AU - Denkena, Berend
AU - Klemme, Heinrich
AU - Stiehl, Tobias H.
N1 - Publisher Copyright: © 2024 Elsevier B.V.. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Tool condition monitoring systems are a prerequisite for autonomous production. However, monitoring small series is challenging as systems usually require references of correct processes for parameterization. As an alternative, these references can be sourced from other machine tools that execute similar processes. Unfortunately, actual processes vary among machine tools, despite machine tools using identical NC-instructions. This work introduces a novel multidimensional position-based online monitoring approach that is designed to overcome process variations among machine tools. This approach utilizes dynamic time warping to match the multidimensional tool paths of machine tools. The results include monitoring of pocket milling on three machine tools using process forces sourced from one, or multiple, other machine tools. A comparison demonstrates that position-based limits for online monitoring, rather than time-based limits, are better suited for transfer among machine tools.
AB - Tool condition monitoring systems are a prerequisite for autonomous production. However, monitoring small series is challenging as systems usually require references of correct processes for parameterization. As an alternative, these references can be sourced from other machine tools that execute similar processes. Unfortunately, actual processes vary among machine tools, despite machine tools using identical NC-instructions. This work introduces a novel multidimensional position-based online monitoring approach that is designed to overcome process variations among machine tools. This approach utilizes dynamic time warping to match the multidimensional tool paths of machine tools. The results include monitoring of pocket milling on three machine tools using process forces sourced from one, or multiple, other machine tools. A comparison demonstrates that position-based limits for online monitoring, rather than time-based limits, are better suited for transfer among machine tools.
KW - dynamic time warping
KW - fleet monitoring
KW - geometrical features
KW - machine tools
KW - tool condition monitoring
UR - http://www.scopus.com/inward/record.url?scp=85208598450&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.08.310
DO - 10.1016/j.procir.2024.08.310
M3 - Conference article
AN - SCOPUS:85208598450
VL - 126
SP - 117
EP - 122
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023
Y2 - 12 July 2023 through 14 July 2023
ER -