Tools
Tools
Al-Azmi, A., Al-Habaibeh, A. 
ORCID: 0000-0002-9867-6011 and Abbas, J.,
2023.
Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations.
The International Journal of Advanced Manufacturing Technology.
ISSN 0268-3768
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Text
1736433_Al-Habaibeh.pdf - Published version Download (3MB) | Preview |
Abstract
This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors' data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.
| Item Type: | Journal article | ||||||
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| Publication Title: | The International Journal of Advanced Manufacturing Technology | ||||||
| Creators: | Al-Azmi, A., Al-Habaibeh, A. and Abbas, J. | ||||||
| Publisher: | Springer | ||||||
| Date: | 27 February 2023 | ||||||
| ISSN: | 0268-3768 | ||||||
| Identifiers: |
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| Rights: | © the author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > School of Architecture, Design and the Built Environment | ||||||
| Record created by: | Jonathan Gallacher | ||||||
| Date Added: | 01 Mar 2023 10:22 | ||||||
| Last Modified: | 01 Mar 2023 10:22 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/48435 |
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