Dressing Monitoring by Acoustic Emission

2005 ◽  
Vol 291-292 ◽  
pp. 195-200 ◽  
Author(s):  
Berend Denkena ◽  
J. Jacobsen ◽  
Niklas Kramer
Keyword(s):  
Acoustic Emission ◽  
Basic Knowledge ◽  
Grinding Wheel ◽  
Acoustic Emission Sensor ◽  
Grinding Processes ◽  
Grinding Machines ◽  
Grinding Time ◽  
Part Production

In modern grinding processes the field of application for vitrified bonded wheels is constantly increasing. Regarding the grinding itself, the advantages of these wheels are obvious. But their ability to be dressed offers further benefits as well. Usually in-machine dressing is favorable. Nevertheless, in-machine dressing causes non-productive-times during which no part production is possible. To reduce this disadvantage, a powerful monitoring is needed in order to minimize the number of needed dressing strokes and to verify the created grinding wheel geometry. The approach applicable for industry is to use an acoustic emission sensor for monitoring, which is usually integrated in modern grinding machines to minimize the air grinding time. This article also provides basic knowledge about Acoustic Emission.

scholarly journals Grinding Wheel Loading Evaluation by Using Acoustic Emission Signals and Digital Image Processing

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4092
Author(s):  
Chien-Sheng Liu ◽  
Yang-Jiun Ou
Keyword(s):  
Image Processing ◽  
Acoustic Emission ◽  
Digital Image Processing ◽  
Digital Image ◽  
Measurement Method ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Gear Grinding ◽  
Wheel Loading ◽  
Grinding Machines

In the manufacturing industry, grinding is used as a major process for machining difficult-to-cut materials. Grinding is the most complicated and precise machining process. For grinding machines, continuous generating gear grinding machines are widely used to machine gears which are essential machine elements. However, due to its complicated process, it is very difficult to design a reliable measurement method to identify the grinding wheel loading phenomena during the grinding process. Therefore, this paper proposes a measurement method to identify the grinding wheel loading phenomenon in the grinding process for continuous generating gear grinding machines. In the proposed approach, an acoustic emission (AE) sensor was embedded to monitor the grinding wheel conditions; an offline digital image processing technique was used to determine the loading areas over the surface of Al2O3 grinding wheels; and surface roughness of the ground workpiece was measured to quantify its machining quality. Then these three data were analyzed to find their correlation. The experimental results have shown that there are two stages of grinding in the grinding process and the proposed measurement method can provide a quantitative grinding wheel loading evaluation from the AE signals online.

An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission

2021 ◽  
Vol 113 (1-2) ◽  
pp. 585-603
Author(s):  
Wenderson N. Lopes ◽  
Pedro O. C. Junior ◽  
Paulo R. Aguiar ◽  
Felipe A. Alexandre ◽  
Fábio R. L. Dotto ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Grinding Wheel ◽  
Short Time Fourier Transform ◽  
Short Time

scholarly journals Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1054
Author(s):  
Guo Bi ◽  
Shan Liu ◽  
Shibo Su ◽  
Zhongxue Wang
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Diamond Wheel ◽  
Feature Representation ◽  
Grinding Wheel ◽  
Time Waveform ◽  
Original Time ◽  
Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

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

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.

scholarly journals Development of a low‐power wireless acoustic emission sensor node for aerospace applications

2021 ◽  
Author(s):  
Stephen Grigg ◽  
Rhys Pullin ◽  
Matthew Pearson ◽  
David Jenman ◽  
Robert Cooper ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Low Power ◽  
Sensor Node ◽  
Acoustic Emission Sensor ◽  
Aerospace Applications

High-temperature (> 1000 °C) acoustic emission sensor

2013 ◽  
Author(s):  
Joseph A. Johnson ◽  
Kyungrim Kim ◽  
Shujun Zhang ◽  
Di Wu ◽  
Xiaoning Jiang
Keyword(s):  
Acoustic Emission ◽  
High Temperature ◽  
Acoustic Emission Sensor
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