Towards Material-Batch-Aware Tool Condition Monitoring
Lutz B, Howell P, Regulin D, Engelmann B, Franke J (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 5
Journal Issue: 4
DOI: 10.3390/jmmp5040103
Abstract
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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Germany (DE)How to cite
APA:
Lutz, B., Howell, P., Regulin, D., Engelmann, B., & Franke, J. (2021). Towards Material-Batch-Aware Tool Condition Monitoring. Journal of Manufacturing and Materials Processing, 5(4). https://doi.org/10.3390/jmmp5040103
MLA:
Lutz, Benjamin, et al. "Towards Material-Batch-Aware Tool Condition Monitoring." Journal of Manufacturing and Materials Processing 5.4 (2021).
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