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Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach


German Terrazas


Panorios Benardos

Cripps Professor of Production Engineering & Head of Research Division


The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.


Terrazas, G., Martínez-Arellano, G., Benardos, P., & Ratchev, S. (2018). Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach. Journal of Manufacturing and Materials Processing, 2(4), Article 72.

Journal Article Type Article
Acceptance Date Oct 10, 2018
Online Publication Date Oct 18, 2018
Publication Date Oct 30, 2018
Deposit Date Nov 30, 2018
Publicly Available Date Nov 30, 2018
Journal Journal of Manufacturing and Materials Processing
Print ISSN 2504-4494
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 2
Issue 4
Article Number 72
Public URL
Publisher URL


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