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Towards Material-Batch-Aware Tool Condition Monitoring

  • In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

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Metadaten
Author:Bastian Engelmann
Persistent identifier:https://doi.org/10.3390/jmmp5040103
ISSN:2504-4494
Parent Title (English):Journal of Manufacturing and Materials Processing
Document Type:Article
Language:English
Date of first Publication:2021/09/27
Publishing Institution:Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt
Release Date:2022/02/14
Tag:manufacturing; material
Volume:5
Issue:4
Institutes and faculty:Fakultäten / Fakultät Wirtschaftsingenieurwesen
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 67 Industrielle Fertigung / 670 Industrielle Fertigung
Regensburger Klassifikation:Technik
Open access colour:Gold
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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