Abstract

Modern CNC machine tools include a number of sensors that collect machine status data. These data are used to control the production process and for control of the CNC machine status. No less importantpart of the production process is also a machine tool. The condition of the cutting tool is important for the production quality and its failure can cause serious problems. Monitoring the condition of thecutting tool is complicated due to its dimensions and working conditions. The article describes how the tool wear can be predicted from the measured values of vibration and pressure by using neural networks.

Details

Title
Use of Neural Networks in Tool Wear Prediction
Author
Kundrík, Juraj; Kočiško, Marek; Pollák, Martin; Telišková, Monika; Bašistová, Anna; Fiala, Zdeněk
Section
Machining Processes and Quality Assurance
Publication year
2019
Publication date
2019
Publisher
EDP Sciences
ISSN
22747214
e-ISSN
2261236X
Source type
Conference Paper
Language of publication
English
DOI
https://doi.org/10.1051/matecconf/201929904003
ProQuest document ID
2322145906
Copyright
© 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.