Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/108519
Title: | Condition monitoring of three-axis ultra-precision milling machine tool for anomaly detection | Authors: | Xu, Z Yip, LWS To, S |
Issue Date: | 2023 | Source: | Procedia CIRP, 2023, v. 119, p. 1210-1215 | Abstract: | Accurately and continuously monitoring ultra-precision machining (UPM) process is the foundation for subsequent diagnosis and optimization, then facilitating energy-saving, efficient production, and high-quality machining. However, comprehensive monitoring of UPM process has hardly been investigated systematically in previous studies. To cover the gap, this study examined the linkages among these parameters monitored in UPM process using a five-layers network for the first time. Subsequently, we proposed an advanced monitoring platform that integrates G-code command, installation sensors, and controller interface. This proposed platform incorporated with anomalies detection algorithm was finally employed and validated on a three-axis ultra-preciisiion miilllliing machiine tooll. Results showed that this proposed platform could successfully achieve anomaly identification using power consumption and X/Y/Z components force signals. | Keywords: | Anomaly detection Condition monitoring Ultra-precision machining |
Publisher: | Elsevier | Journal: | Procedia CIRP | EISSN: | 2212-8271 | DOI: | 10.1016/j.procir.2023.04.012 | Description: | 33rd CIRP Design Conference 2023, 17-19 May 2023, Sydney, Australia | Rights: | © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) The following publication Xu, Z., Yip, L. W. S., & To, S. (2023). Condition monitoring of three-axis ultra-precision milling machine tool for anomaly detection. Procedia CIRP, 119, 1210-1215 is available at https://doi.org/10.1016/j.procir.2023.04.012. |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S2212827123006431-main.pdf | 2.61 MB | Adobe PDF | View/Open |
Page views
36
Citations as of Oct 13, 2024
Downloads
8
Citations as of Oct 13, 2024
SCOPUSTM
Citations
1
Citations as of Oct 17, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.