Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysis

Niko Turšič (Author), Simon Klančnik (Author)

Abstract

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.

Keywords

nadzor obrabe orodja;umetna inteligenca;LSTM;tool condition monitoring;artificial intelligence;LSTM neural network;

Data

Language:	English
Year of publishing:	2024
Typology:	1.01 - Original Scientific Article
Organization:	UM FS - Faculty of Mechanical Engineering
Publisher:	MDPI
UDC:	621.941.025:004.8
COBISS:	193343491
ISSN:	1424-8220
Views:	32
Downloads:	4
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	nadzor obrabe orodja;umetna inteligenca;umetna inteligenca;LSTM;
Type (COBISS):	Article
Pages:	13 str.
Volume:	ǂVol. ǂ24
Issue:	ǂiss. ǂ8, [article no.] 2490
Chronology:	April 2024
DOI:	10.3390/s24082490
ID:	23492604