20.500.12556/DKUM-88422 Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysis In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools. tool condition monitoring artificial intelligence LSTM neural network nadzor obrabe orodja umetna inteligenca umetna inteligenca LSTM true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/sensors © 2024 by the authors Članek v reviji 2024-04-22 13:12:31 2024-04-22 13:12:32 2026-01-13 11:36:38 0000-00-00 00:00:00 2024 0 0 13 str. iss. 8, [article no.] 2490 Vol. 24 April 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-02-23 2024-04-11 2024-04-12 621.941.025:004.8 193343491 10.3390/s24082490 1424-8220 sensors-24-02490-v2.pdf sensors-24-02490-v2.pdf 1 5CD99E562F362046587AD6F56F742F1E c290f8ad39b525e6340533e7d1e92172457f8cbbb7203fc8f40728252bf7b8d2 8c7b6fb5-0099-11ef-a521-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=178824 https://www.mdpi.com/1424-8220/24/8/2490 0 a6a47ca9-0099-11ef-a521-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=178826 Fakulteta za strojništvo 0 0 0