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Erschienen in: The International Journal of Advanced Manufacturing Technology 11-12/2020

07.05.2020 | ORIGINAL ARTICLE

Technical data-driven tool condition monitoring challenges for CNC milling: a review

verfasst von: Shi Yuen Wong, Joon Huang Chuah, Hwa Jen Yap

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 11-12/2020

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Abstract

CNC milling is a highly complex machining process highly valued in various industries, including the automotive and aerospace industries. With the increasing competition, manufacturers are aiming to keep maintenance costs low while ensuring high levels of manufacturing equipment reliability. It is also highly important for them to maximize the service life of each cutting tool by avoiding premature replacements while minimizing the risks of scrap due to tool breakage. This calls for the need for a condition-based maintenance approach and more powerful, flexible and robust tool condition monitoring (TCM) techniques with minimal reliance on subjective diagnosis based on the expert knowledge. This paper discusses the technical aspects of recent developments in state-of-the-art TCM techniques and current challenges which limit the viability of TCM in real-life industrial applications. The technical challenges in modern TCM were split into two major groups of problems: (1) challenges in data processing and (2) issues regarding tool wear model performance. Current methodologies to overcome issues in each of the sections in this paper are discussed and, where possible, compared to highlight their respective advantages and disadvantages. Finally, this paper concludes with a discussion on possible trends in TCM development and interesting avenues for future research.

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Metadaten
Titel
Technical data-driven tool condition monitoring challenges for CNC milling: a review
verfasst von
Shi Yuen Wong
Joon Huang Chuah
Hwa Jen Yap
Publikationsdatum
07.05.2020
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 11-12/2020
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-020-05303-z

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