A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment
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comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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INVESTIGACIONMetadata
Title
A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid DeploymentAuthor (s)
Date
2023Publisher
MDPIBibliographic citation
Failing, J.M.; Abellán-Nebot, J.V.; Benavent Nácher, S.; Rosado Castellano, P.; Romero Subirón, F. A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment. Processes 2023, 11, 668. https:// doi.org/10.3390/pr11030668Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/2227-9717/11/3/668Version
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Abstract
Tool condition monitoring (TCM) systems are key technologies for ensuring machining
efficiency. Despite the large number of TCM solutions, these systems have not been implemented
in industry, especially in small- ... [+]
Tool condition monitoring (TCM) systems are key technologies for ensuring machining
efficiency. Despite the large number of TCM solutions, these systems have not been implemented
in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need
for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable
solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0
is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with
plug and play capabilities, minimum interruption time during setup and minimal experimental
tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that
are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving
operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an
edge-computing node to extract a similarity index for tool wear classification; a machine learning
node based on a BeagleBone Black board that builds the machine learning model using a Python
script; and an IoT platform to provide the communication infrastructure and register all data for
future analytics. Experimental results on a CNC lathe show that a logistic regression model applied
on the machine learning node can provide a low-cost and straightforward solution with an accuracy
of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours
are required for deployment. Practitioners in SMEs can find the proposed approach interesting since
fast results can be obtained and more complex analysis could be easily incorporated while production
continues using the operator’s feedback from the mobile app. [-]
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Processes 2023, 11, 668Funder Name
Universitat Jaume I
Project code
UJI-B2020-33 | UJI-B2022-49
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