Monitoring system for cutting tool failure using an acoustic emission sensor.

In the field of machinery manufacture, broken state at the time of the cutting tool in cutting metal, recognition has always been a study is of great significance. Currently, for the state of tool wear and collapse edge damage identification method already has a mature experience. However the existing condition monitoring methods are often used in accuracy and convenience has limitations, this paper USES the acoustic emission technology, as a kind of integrated online test sys tem design lay the foundation. This paper aimed at the sensor in the wireless transmission module, the performance characteristics of tool condition monitoring system of the main structure was designed, and then by acoustic emission signal from the cutting tool in cutting process as the research object, studies the cutting tool characteristics of acoustic emission signal under different damage state, for the on-line monitoring system design and calibration to provide theoretical support.

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Cutting Process Monitoring by Means of Acoustic Emission Method; Part I - New Approach of Acoustic Emission Sensor; Part II - Transformation of Acoustic Emission into Audible Sound

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.13-14.105 ◽

2006 ◽

Vol 13-14 ◽

pp. 105-110 ◽

Cited By ~ 1

Author(s):

Jan Zizka ◽

Petr Hana ◽

L. Hamplova ◽

Z. Motycka

Keyword(s):

Acoustic Emission ◽

Cutting Tool ◽

Cutting Tools ◽

Data Gathering ◽

Modern Society ◽

Body Part ◽

Electrical Signal ◽

Machining Process ◽

Acoustic Emission Sensor ◽

Audible Sound

Development of modern society is converging to a status where many human actions can be performed by machines. To achieve production without human intervention, machines require artificial receptors. Data gathering for processing and analysis of signals, together with determination of feedback reactions can be achieved by a suitable decision maker unit. A sensed value suited to this so-called intelligent sensing process would be the acoustic emission signal. In the case of intelligent cutting tools this would require miniature highly sensitive sensors integrated into the cutting tool body. Part I of this paper deals with the possibility of practical usage of the piezoelectric properties of copolymer foils for the acoustic emission sensor as a transducer of a mechanical surface wave into electrical signal. Part II of the paper deals with the most fundamental requirement for monitoring of cutting conditions during machining, i.e. excellent processing of measured data. Data obtained from machining process obtained by means of acoustic emission sensors, as discussed in the first part of this article, have high-frequency and continuous character of a white noise. These data are very difficult to process. New apparatus for transformation of acoustic emission into audible sound in the workplace is presented. The first stage of processing is by listening to transformed data it is subjectively possible to recognize differences in audible spectrum, corresponding to different states of the cutting tool. The second step is visualization of the differences via the fast Fourier transform (FFT) in the spectrum graphic chart.

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Online PCBN tool failure monitoring system based on acoustic emission signatures

IEE Proceedings - Science Measurement and Technology ◽

10.1049/ip-smt:19952071 ◽

1995 ◽

Vol 142 (5) ◽

pp. 404-410 ◽

Cited By ~ 7

Author(s):

T.W. Liao ◽

Q. Zou ◽

M.E. Zodhi ◽

L. Mann

Keyword(s):

Acoustic Emission ◽

Monitoring System ◽

Tool Failure ◽

Pcbn Tool ◽

Failure Monitoring

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Live demonstration: Energy autonomous wireless valve leakage monitoring system with acoustic emission sensor

2017 IEEE SENSORS ◽

10.1109/icsens.2017.8234026 ◽

2017 ◽

Author(s):

Colm McCaffrey ◽

Teuvo Sillanpaa ◽

Henrik Huovila ◽

Joona Nikunen ◽

Sami Hakulinen ◽

...

Keyword(s):

Acoustic Emission ◽

Monitoring System ◽

Acoustic Emission Sensor ◽

Valve Leakage ◽

Live Demonstration ◽

Leakage Monitoring

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Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

Journal of Vibration and Control ◽

10.1177/10775463211016130 ◽

2021 ◽

pp. 107754632110161

Author(s):

Aref Aasi ◽

Ramtin Tabatabaei ◽

Erfan Aasi ◽

Seyed Mohammad Jafari

Keyword(s):

Acoustic Emission ◽

Condition Monitoring ◽

Time Domain ◽

Central Moment ◽

Rolling Element Bearings ◽

Test Rig ◽

Acoustic Emission Sensor ◽

Outer Race ◽

Common Time ◽

Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

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Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

Applied Sciences ◽

10.3390/app11157045 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7045

Author(s):

Ming-Chyuan Lu ◽

Shean-Juinn Chiou ◽

Bo-Si Kuo ◽

Ming-Zong Chen

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Frequency Domain ◽

Monitoring System ◽

Welding Quality ◽

Steel Sheets ◽

Signal Features ◽

Ae Signal ◽

Laser Microwelding ◽

Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

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