| Naslov: | A cloud-based system for the optical monitoring of tool conditions during milling through the detection of chip surface size and identification of cutting force trends |
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| Avtorji: | ID Župerl, Uroš (Avtor)
ID Stępień, Krzysztof (Avtor)
ID Munđar, Goran (Avtor)
ID Kovačič, Miha (Avtor) |
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| Datoteke: |  processes-10-00671.pdf (5,65 MB)
MD5: B47A5B36F463F8418C7ECB7E079EBD23
 https://www.mdpi.com/2227-9717/10/4/671
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FS - Fakulteta za strojništvo
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| Opis: | This article presents a cloud-based system for the on-line monitoring of tool conditions in
end milling. The novelty of this research is the developed system that connects the IoT (Internet of
Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical
system for the detection of cutting chip size. The optical system takes care of the acquisition and
transfer of signals regarding chip size to the IoT application, where they are used as an indicator
for the determination of tool conditions. In addition, the novelty of the presented approach is in
the artificial intelligence integrated into the platform, which monitors a tool’s condition through
identification of the current cutting force trend and protects the tool against excessive loading by
correcting process parameters. The practical significance of the research is that it is a new system for
fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of
a more cost-effective sensor, improves machining efficiency and allows remote process monitoring
on mobile devices. A machining test was performed to verify the feasibility of the monitoring
system. The results show that the developed system with an ANN (artificial neural network) for the
recognition of cutting force patterns successfully detects tool damage and stops the process within
35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the
identification of tool breakage, which verifies the effectiveness and practicality of the approach. |
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| Ključne besede: | machining, end milling, tool condition monitoring, chip size detection, cutting force trend identification, visual sensor monitoring, cloud manufacturing technologies |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 24.02.2022 |
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| Datum sprejetja članka: | 28.03.2022 |
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| Datum objave: | 30.03.2022 |
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| Založnik: | MDPI AG |
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| Leto izida: | 2022 |
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| Št. strani: | 21 str. |
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| Številčenje: | Vol. 10, iss. 4 |
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| PID: | 20.500.12556/DKUM-92264  |
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| UDK: | 621.914.1 |
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| COBISS.SI-ID: | 102886659 |
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| DOI: | 10.3390/pr10040671 |
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| ISSN pri članku: | 2227-9717 |
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| Avtorske pravice: | © 2022 by the authors |
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| Datum objave v DKUM: | 26.03.2025 |
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| Število ogledov: | 167 |
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| Število prenosov: | 14 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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