Multi-sensor data fusion framework for CNC machining monitoring

Duro, Joao A., Padget, Julian A., Bowen, Chris R., Kim, Alicia ORCID: https://orcid.org/0000-0002-5629-2466 and Nassehi, Aydin 2016. Multi-sensor data fusion framework for CNC machining monitoring. Mechanical Systems and Signal Processing 66-67 , pp. 505-520. 10.1016/j.ymssp.2015.04.019

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Abstract

Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Elsevier
ISSN:	0888-3270
Date of Acceptance:	16 April 2015
Last Modified:	01 Nov 2022 10:24
URI:	https://orca.cardiff.ac.uk/id/eprint/91339

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