An intelligent sensor system approach for reliable tool flank wear recognition

Niu, Yan; Wong, Yoke San; Gao, Hong

doi:10.1007/bf01322215

Cited by 37 publications

(17 citation statements)

References 9 publications

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“…2. AE together and/or combined with other sensors: tool wear [24][25][26][27][28][29], tool breakage detection [30,31], surface roughness prediction [32], and dimensionality accuracy and surface roughness [33].…”

Section: Acoustic Emissions Monitoring In Machiningmentioning

confidence: 99%

Multi-sensor data fusion framework for CNC machining monitoring

Duro

Padget

Bowen

et al. 2016

Mechanical Systems and Signal Processing

112

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a b s t r a c tReliable 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.

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Section: Acoustic Emissions Monitoring In Machiningmentioning

confidence: 99%

Multi-sensor data fusion framework for CNC machining monitoring

Duro

Padget

Bowen

et al. 2016

Mechanical Systems and Signal Processing

112

View full text Add to dashboard Cite

show abstract

“…This approach has been employed in the past in other areas like in smart homes, appliances in smart cars, in smart production machines, in biomedical applications [35] like hand vibration measurement [25], stress management [36], in recognizing the tool flank wear state over a range of cutting conditions [17], in robotics etc., but is not used in WSN nodes yet. This approach introduces new challenges (e.g.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Humidity Sensor for Wireless Sensor Network Agricultural Application

Shinghal¹,

Noor²,

Srivastava³

et al. 2011

IJWMN

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Wireless Sensor Networks (WSN) is now widely used in precision agriculture applications. Sensors play an important role in WSN.The traditional humidity sensors employed in the agriculture have a drawback that they acquire a large amount of data which is to be transmitted or processed. Whereas the intelligent sensor proposed in this paper uses a conventional sensor and an embedded processor that can process the data acquired using algorithms to reduce the amount of data collected, to extract only relevant information and to present this information in a format which minimizes post-processing latency. As a result of which the final processing end has to perform a little computation that too only on features (like significant changes in humidity) rather on the huge data hence reducing the power consumption. The intelligent humidity sensor reduces the amount of data processed by 50% (depending upon humidity variations) and thereby also reducing the power consumption.

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“…* The feature extraction condenses the remaining information in a few wear-sensitive values (see, e.g. [134,184,200]) which can be used as inputs of wear models (e.g. neural networks) at the following level.…”

Section: A Generic Sensor Fusion Architecture For the Description Of mentioning

confidence: 99%

“…Very often, different kinds of methods are combined, e.g. whenever statistical features of wavelet coefficients [145,[161][162][163] or spectral coefficients [134,135], respectively, are determined. In many publications it is simply not recognisable, which features are used.…”

Section: Types Of Featuresmentioning

confidence: 99%