Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-14364
Authors: Ströbel, Robin
Bott, Alexander
Wortmann, Andreas
Fleischer, Jürgen
Title: Monitoring of tool and component wear for self-adaptive Digital Twins : a multi-stage approach through anomaly detection and wear cycle analysis
Issue Date: 2023
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 27
metadata.ubs.publikation.source: Machines 11 (2023), No. 1032
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-143839
http://elib.uni-stuttgart.de/handle/11682/14383
http://dx.doi.org/10.18419/opus-14364
ISSN: 2075-1702
Abstract: In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Files in This Item:
File Description SizeFormat 
machines-11-01032-v2.pdf11,07 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons