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Open AccessArticle

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
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Author to whom correspondence should be addressed.
Current address: Institute for Advanced Manufacturing, Jubilee Campus, University of Nottingham, Nottingham NG7 2GX, UK.
J. Manuf. Mater. Process. 2018, 2(4), 72; https://doi.org/10.3390/jmmp2040072
Received: 30 August 2018 / Revised: 5 October 2018 / Accepted: 10 October 2018 / Published: 18 October 2018
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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%. View Full-Text
Keywords: tool wear; dry machining; big data classification; convolutional neural networks tool wear; dry machining; big data classification; convolutional neural networks
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Terrazas, G.; Martínez-Arellano, G.; Benardos, P.; Ratchev, S. Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach. J. Manuf. Mater. Process. 2018, 2, 72.

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