Tool Monitoring with Acoustic Emission Industrial Results and Future Prospects

Souquet, Pierre Jean; Gsib, N.; Deschamps, M.; Roget, J.; Tanguy, J.C.; Geslot, R.

doi:10.1016/s0007-8506(07)62553-0

Cited by 18 publications

(5 citation statements)

References 2 publications

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“…And then use the acoustic emission, acceleration sensor to collected signals in processing by Labview acquisition platform, and complete data measurement collection and storage capabilities on this platform, Overall system block diagram shown in Figure 2. Signal Acquisition Card is used Advantech PCI-1714UL high-speed cards and PCI-1710 low-speed card, PCI-21714UL card collecting vibration and acoustic emission with 3MHz sample rate, The major advantage of using AE to monitor tool condition is that the frequency range of the AE signal is much higher than machine vibrations and environmental noises [3][4][5][6], Using the PCI-1710 card used to collect temperature signals with16K sample rate during the hard dry turning, collecting temperature with 100 sample rate when need control temperature, 1243 motion control card control electro-hydraulic digital valve switch. Sensors were used to optics CT Infrared Thermometer, built-in ICP accelerometer CA2YD21182, SR800A-type acoustic emission sensors, the sensors are equipped with the appropriate signal conditioning modules.…”

Section: Measuring Systemmentioning

confidence: 99%

Based on Multi-Sensor Tool Steel Hard Turning Process Monitoring and Controlling

Wang

Zhong

et al. 2010

AMR

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Through a set of electro-hydraulic digital valve as the core of the fuzzy control system to provide an appropriate amount of cooling fluid, the hard turning process temperature can be controlled at any set temperature. The use of the signal acquisition system based on virtual instrument, acquisition acoustic emission signals, vibration signals and temperature signals during the hard turning process. The temperature signal is processed by wavelet transform ,after vibration and acoustic emission signals be processed by wavelet packet decomposition and energy method, it is found that through providing appropriate cooling fluid can control temperatures in the processing and decrease the amplitude of vibration and acoustic emission signal, it also means that we can improve the quality of processing, and prolong the life of tool.

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Section: Measuring Systemmentioning

confidence: 99%

Based on Multi-Sensor Tool Steel Hard Turning Process Monitoring and Controlling

Wang

Zhong

et al. 2010

AMR

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“…The continuous AE signal is related to low frequency and low amplitude signals generated from primary, secondary and tertiary deformation zones [8]. The major advantage of using AE signals for metal cutting is that the frequency range observed during machining is found to be much higher than the environmental noises and vibrations [10].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

Prakash

Ravisankar

Kanthababu

2014

AMR

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In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AERMS show uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AERMS and specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

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“…TCM is the key technology in automated manufacturing [1][2][3]. A lot of research work has been conducted and published within the past decades, including methods and rules for signal acquisition [4,5], feature extraction to indicate tool condition [3][4][5][6] and decision-making for tool replacement.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management

Yao

Lü

Yuan

et al. 2004

MSF

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This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.

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Tool Monitoring with Acoustic Emission Industrial Results and Future Prospects

Cited by 18 publications

References 2 publications

Based on Multi-Sensor Tool Steel Hard Turning Process Monitoring and Controlling

Based on Multi-Sensor Tool Steel Hard Turning Process Monitoring and Controlling

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management

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