Multi-sensor data fusion framework for CNC machining monitoring

Joao A. Duro, Julian A. Padget, Chris R. Bowen, H. Alicia Kim, Aydin Nassehi

Research output: Contribution to journalArticlepeer-review

119 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)505-520
JournalJournal of Mechanical Systems and Signal Processing
Early online date2 Jul 2015
Publication statusPublished - Jan 2016


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