scholarly journals The Wear Detection of Mill-Grinding Tool Based on Acoustic Emission Sensor

2021 ◽  
Author(s):  
Wuzhen Huang ◽  
Yuan Li ◽  
Xian Wu ◽  
Jianyun Shen
Keyword(s):  
Acoustic Emission ◽  
Characteristic Parameter ◽  
Waveform Analysis ◽  
Parameter Method ◽  
Large Area ◽  
Acoustic Emission Sensor ◽  
Processing Efficiency ◽  
Severe Wear ◽  
Emission Signal ◽  
Abrasive Grains

Abstract The monitoring of tool wear plays an important role in improving the processing efficiency and reducing the production cost of enterprises. This paper is focused on the detection of electroplated diamond mill-grinding tools by using the acoustic emission sensor. The wear stages of mill-grinding tools are divided into three parts, namely initial wear stage, normal wear stage, and severe wear stage. The characteristic parameter method and the waveform analysis method are applied to analyze the acoustic emission signals. The wear characteristics of the tool and workpiece in different wear stages are observed and analyzed. The results indicate that the acoustic emission waveform is relatively stable in the initial wear stage, and the continuous acoustic emission signal is dominated. Moreover, the diamond abrasive grains are mainly worn and slightly broken in the normal wear stage, and there are some pits on the machined workpiece surface after the initial wear stage. In the severe wear stage, most of the abrasive grains are broken or broken in a large area, and there are burst acoustic emission signals in the waveform.

Investigation of Stamping Tool Wear Initiation at Microscopic Level Using Acoustic Emission Sensors

2019 ◽  
Vol 794 ◽  
pp. 285-294 ◽  
Author(s):  
Vignesh V. Shanbhag ◽  
Bernard F. Rolfe ◽  
Narayanan Arunachalam ◽  
Michael P. Pereira
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Adhesive Wear ◽  
Microscopic Level ◽  
Mean Frequency ◽  
Severe Wear ◽  
Emission Signal ◽  
Early Stages ◽  
Acoustic Emission Sensors

Stamping tools are prone to an adhesive wear mode called galling. Adhesive wear on stamping tools can degrade the product quality and can affect the mass production. Even a small improvement in the maintenance process is beneficial for the stamping industry. Therefore, this study will focus on understanding and detecting the initiation of tool wear at the microscopic level in sheet metal stamping using acoustic emission sensors. Stamping tests were performed using a semi-industrial stamping process, which can perform clamping, piercing, stamping and trimming in a single cycle. The stamping test was performed using a high strength low alloy sheet steel and D2 tool steel for dry and lubricated conditions. The acoustic emission signal was recorded for each stamped part until severe wear on the dies was observed. These acoustic emission signals were later analyzed using time and frequency domain features. The time domain features such as peak, RMS, kurtosis and skewness could identify significant changes in the acoustic emission signal only when the severe wear was observed on the stamped parts for both dry and lubricated conditions. However, this study has identified that a frequency feature – known as mean-frequency estimate – could identify early stages of wear initiation at the microscopic level. Evidence of this early stage of wear on the part surfaces was not clearly visible to the naked eye, and could only be clearly observed via surface measurement instruments such as an optical profilometer. The sidewalls of the stamped parts corresponding to the initial change in AE mean-frequency trend were qualitatively correlated with 3D profilometer scans of the stamped parts, to show that AE mean-frequency can indicate the initial minor scratches on the sidewalls of the stamped parts due to the galling wear on the die radii surfaces. The results from this study can be used to develop a methodology to determine the very early stages of stamping tool wear, providing a strong basis for condition monitoring in the stamping industry.

The EMD Analysis AE Signals of Rock Failure under Uniaxial Compression

2014 ◽  
Vol 571-572 ◽  
pp. 845-852
Author(s):  
Tian Jun Zhang ◽  
Sheng Hong Yu ◽  
Jin Hu Ren ◽  
Wei Cui
Keyword(s):  
Acoustic Emission ◽  
Uniaxial Compression ◽  
Acoustic Emission Signal ◽  
Wavelet Packet ◽  
Testing Machine ◽  
Waveform Analysis ◽  
Mode Analysis ◽  
Emission Signal ◽  
Mode Decomposition ◽  
Emd Method

The wavelet packet basis is difficult to be extracted by wavelet analysis at present. To solve this problem, an experiment of Acoustic Emission under uniaxial compression is conducted by SAEU2S acoustic Emission system and Electro-hydraulic servo universal testing machine and the method of empirical mode analysis is adopted to explore the acoustic emission signal in this paper. Firstly with the method of empirical mode decomposition, the acoustic emission signal is decomposed into the forms of intrinsic mode function with several local time scale and residual components, and then these data is analyzed. After the noise-reducing IMF and residual components are refactored, the error between the final and the initial reconstruction signals is less than 10-6. The experiment indicates that the EMD method is effective in processing the local rock acoustic emission signals. The EMD method also provides an efficient way to predict deformation trend of rock damage through deformation of waveform analysis.

Detection of Misfire in a Six-Cylinder Diesel Engine Using Acoustic Emission Signals

2018 ◽  
Author(s):  
Mohammad Jafari ◽  
Pietro Borghesani ◽  
Puneet Verma ◽  
Ashkan Eslaminejad ◽  
Zoran Ristovski ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Diesel Engine ◽  
Sensor Technology ◽  
Angular Domain ◽  
Acoustic Emission Sensor ◽  
Emission Signal ◽  
Crank Angle ◽  
Ae Signal ◽  
Envelope Spectrum ◽  
Short Time

This study will focus on the detection of misfire using Acoustic emission sensor in a multi-cylinder diesel engine. Detection of misfire is important since this malfunction can cause the engine to stall in a short time. In order to investigate the misfire, an experimental engine was run with and without injection of the fuel in the first cylinder. The acoustic emission signal was acquired synchronously with the crank angle signal, in order to have a reference for the transformation from time to angular domain. The AE signal was then processed using the squared envelope spectrum to highlight angle-periodic modulations in the signal’s power (cyclic bursts). This study will present the effectiveness of this combination of sensor technology and signal processing to detect misfire in a six-cylinder diesel engine connected to a hydraulic dynamometer.

A Study on Monitoring System for Grinding Wheel Status Based on Acoustic Emission Technology

2011 ◽  
Vol 52-54 ◽  
pp. 2051-2055
Author(s):  
Pei Jiang Li ◽  
Ting You
Keyword(s):  
Acoustic Emission ◽  
High Speed ◽  
Grinding Wheel ◽  
Signal Acquisition ◽  
Wheel Wear ◽  
Processing Efficiency ◽  
Emission Signal ◽  
Grinding Wheel Wear ◽  
Upper Computer

The grinding wheel wear status is an important guarantee for the processing efficiency and processing quality of precision and super precision grinding. In this paper, a USB acoustic emission signal acquisition system is designed for online monitoring of grinding wheel status. In the system, CPLD is used as the controller, and a high-speed A/D converter is used to implement the synchronous acquisition of acoustic emission array signals. The collected data are sent into FIFO, and CY7C68013A is used for USB data transmission with upper computer. The sampling frequency of the system can be 10 MHz, and USB transmission speed can reach 40M/S. It is proved that it can meet the monitoring requirements of grinding wheel wear status well by the grinding processing.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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