Degradation monitoring of machine tool ballscrew using deep convolution neural network

Nurudeen Alegeh, Abubaker Shagluf, Andrew Longstaff, Simon Fletcher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-value manufacturing often requires a high level of accuracy. While this may be an achievable aim, the demands of consumers and end-users are also for the often competing targets of lower cost, greater efficiency and resource-lean products. Notwithstanding the ambition for higher accuracy, increased availability of production machines is a fundamental requirement to maintain competitiveness in the manufacturing industry. Ballscrews are a fundamental part of the transmission system for most high-value machine tools. They are therefore integral to the positional accuracy and performance of the machine and also represent a weaklink in terms availability. Hence, the state of the ballscrew is essential in determining machine accuracy and availability. This work proposes a deep learning approach for ballscrew performance monitoring. The technique works such that remedial activities can be scheduled and carried out when degradation is detected before breakdown occurs. The deep learning algorithm uses convolution to distinguish between a worn and good ballscrew in a machine tool. The technique was tested on a five-axis gantrytype machine tool with two parallel axis ballscrew. The results from the test carried out indicates that an overall accuracy of 94 % can be achieved with this technique.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology
Subtitle of host publicationEuspen 2020, Virtual Conference
EditorsR. K. Leach, D. Billington, C. Nisbet, D. Phillips
Publishereuspen
Pages209-212
Number of pages4
ISBN (Print)9780995775176
Publication statusPublished - 12 Jun 2020
Event20th International Conference of the European Society for Precision Engineering and Nanotechnology - Virtual Conference
Duration: 8 Jun 202012 Jun 2020
Conference number: 20
https://www.euspen.eu/events/vc-2020/

Conference

Conference20th International Conference of the European Society for Precision Engineering and Nanotechnology
Abbreviated titleICE20
Period8/06/2012/06/20
Internet address

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    Alegeh, N., Shagluf, A., Longstaff, A., & Fletcher, S. (2020). Degradation monitoring of machine tool ballscrew using deep convolution neural network. In R. K. Leach, D. Billington, C. Nisbet, & D. Phillips (Eds.), Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology: Euspen 2020, Virtual Conference (pp. 209-212). [O4.03] euspen. https://www.euspen.eu/knowledge-base/ICE20110.pdf