dc.contributor.advisor	Kraus Jan, Ing. Ph.D. :56646	cs
dc.contributor.author	Saatçi, Anil Can	cs
dc.contributor.referee	Bubla Viktor, Ing. :68452	cs
dc.date.accessioned	2025-07-14T17:20:22Z
dc.date.available	2025-07-14T17:20:22Z
dc.date.committed	9.5.2025	cs
dc.date.defense	10.6.2025	cs
dc.date.issued	2025-06-10	cs
dc.date.submitted	10.2.2025	cs
dc.description.abstract	This paper explores possibility of CNC machine condition monitoring in real life, using machine learning algorithms running on embedded devices. CNC machines are vital in most modern manufacturing facilities. Some of this CNC machines utilize carbide, ceramic or high-speed-steel cutting tools to drill, mill, thread, cut and shape materials, which are often metal. These cutting tools gets gradually worn-out during machining. A STM324755ZI-Q based device has been developed which utilizes a microphone in order to determine the cutting tool wear. This device can be used for both data gathering and run-ning the machine learning algorithm which can determine the condition of a cutting tool and inform operators about it. In this research not only a device is developed, also the most efficient way of data gathering and data processing is researched therefore, the optimum way on how to gather data for predictive maintenance of CNC machines using a microphone and how to implement it is determined.	cs
dc.description.abstract	This paper explores possibility of CNC machine condition monitoring in real life, using machine learning algorithms running on embedded devices. CNC machines are vital in most modern manufacturing facilities. Some of this CNC machines utilize carbide, ceramic or high-speed-steel cutting tools to drill, mill, thread, cut and shape materials, which are often metal. These cutting tools gets gradually worn-out during machining. A STM324755ZI-Q based device has been developed which utilizes a microphone in order to determine the cutting tool wear. This device can be used for both data gathering and running the machine learning algorithm which can determine the condition of a cutting tool and inform operators about it. In this research not only a device is developed, also the most efficient way of data gathering and data processing is researched therefore, the optimum way on how to gather data for predictive maintenance of CNC machines using a microphone and how to implement it is determined.	en
dc.format	52	cs
dc.identifier.uri	https://dspace.tul.cz/handle/15240/177341
dc.language.iso	AN	cs
dc.subject	Machine learning	cs
dc.subject	CNC machine	cs
dc.subject	Cutting tools	cs
dc.subject	Embedded development	cs
dc.subject	Signal processing	cs
dc.subject	Microphone	cs
dc.subject	Predictive maintenance	cs
dc.title	CNC Machine Tool Condition Monitoring	cs
dc.title	CNC Machine Tool Condition Monitoring	en
dc.type	diplomová práce	cs
local.degree.abbreviation	Navazující	cs
local.identifier.author	M23000156	cs
local.identifier.stag	49050	cs

CNC Machine Tool Condition Monitoring

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