Analysis of Acoustic Emission Signals in Machining

1999 ◽  
Vol 121 (4) ◽  
pp. 568-576 ◽  
Author(s):  
S. T. S. Bukkapatnam ◽  
S. R. T. Kumara ◽  
A. Lakhtakia
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Flank Wear ◽  
Signal Representation ◽  
Machining Processes ◽  
Tight Set ◽  
Multilayer Neural Networks ◽  
Active Research ◽  
Signal Characterization ◽  
Ae Signals

Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation.

Acoustic Emission Sensing of Tool Wear in Face Milling

1987 ◽  
Vol 109 (3) ◽  
pp. 234-240 ◽  
Author(s):  
E. N. Diei ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Cutting Forces ◽  
Flank Wear ◽  
Face Milling ◽  
Processing Scheme ◽  
Acoustic Emission Sensing ◽  
Vertical Milling Machine ◽  
Time Domain Averaging ◽  
Ae Signals

Acoustic Emission (AE) signal analysis was applied to on-line sensing of tool wear in face milling. Cutting tests were conducted on a vertical milling machine. AE signals, feed and normal components of cutting force and flank wear were measured and compared. A signal processing scheme for intermittent cutting forces and AE signals, based on the concept of time domain averaging (TDA) is proposed. The results indicate that both AE and cutting forces have parameters that correlate closely with flank wear.

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

1997 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
S. V. Kamarthi ◽  
S. R. T. Kumara ◽  
P. H. Cohen
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fourier Transform ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Flank Wear ◽  
Wavelet Representation ◽  
Power Spectral ◽  
Frequency Components ◽  
Ae Signals

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

Analysis of 1D Abrasive Vibratory Finishing Using Acoustic Emission

2013 ◽  
Author(s):  
Pradeep Kumar Prakasam ◽  
Sathyan Subbiah
Keyword(s):  
Acoustic Emission ◽  
Monitoring And Control ◽  
Machining Processes ◽  
Vibratory Finishing ◽  
One Dimensional ◽  
Oblique Impacts ◽  
The Media ◽  
The One ◽  
And Control ◽  
Ae Signals

Acoustic emission (AE) is a widely used non-destructive method for monitoring and control of machining processes. Vibratory finishing is a surface modification process used for polishing, deburring and finishing of components (aerospace, automotive and other industries). The polishing action takes place due to the action of abrasive particles called media on the components subjected to finishing. The media motion is complex and involves a combination of normal and oblique impacts, scratching and rolling. This work deals with the characterization of basic types of media contact occurring in the vibratory finishing process using acoustic emission signals. A novel one dimensional vibratory simulator was developed for this purpose using a tribometer setup. The one dimensional simulator was used to differentiate between the normal and scratching types of media contact and corresponding AE signals were measured. The preliminary results shows that the AE signals obtained for normal and scratching type of contacts are different. In addition to this, AE signals were used to characterize the amount of media.

Correlative Flank Wear Analysis of Single Point Turning Inserts Using Acoustic Emission and Artificial Intelligence Techniques

2008 ◽  
Author(s):  
R. Srinidhi ◽  
Vishal Sharma ◽  
M. Sukumar ◽  
C. S. Venkatesha
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Flank Wear ◽  
Machining Process ◽  
Work Piece ◽  
Machining Parameters ◽  
Wear Analysis ◽  
Tools Wear ◽  
Coated Carbide ◽  
Ae Signals

Wear mechanism of a cutting tool is highly complex in that the processes of tool wear results from interacting effect of machining configurations. Various output generated by the study and analysis of each tool is extremely useful in analyzing the tool characteristics in general and to make efforts to obtain the estimated tool life in particular. The gradual process of tool wear has adverse influence on the quality of the surface generated and on the design specifications in the work piece dimensions and geometry, and causes, at the worst case, machine breakdown. Advanced manufacturing demands proper use of the right tool and emphasizes the need to check the wear rate. A scientific method of obtaining conditions for an optimal machining process with proper tools and control of machining parameters is essential in the present day manufacturing processes. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the indirect methods of tool wear analysis and monitoring is based on the acoustic emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process and provides a means of evaluating the wear of cutting tools. Wear parameters obtained in the process are analyzed with the output generated by using Multi Layer Perceptron (MLP) based back propagation technique and Adaptive Neuro Fuzzy Interference System (ANFIS). The results obtained from these methods are correlated for the actual and predicted wear. Experiments have been conducted on EN8 and, EN24 using Uncoated Carbide, Coated carbide and Ceramic inserts (Kennametal, India make) on a high speed lathe for the most appropriate cutting conditions. The AE signal analysis (considering signal parameters such as, ring down count (RDC), rise time (RTT), event duration (ED) and energy (EG). Flank wear in tools and corresponding cutting forces for each of the trials are measured and are correlated for various combinations of tools and materials of work piece.

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

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.

scholarly journals Acoustic Emission Testing and Ib-Value Analysis of Ultraviolet Light-Irradiated Fiber Composites

2021 ◽  
Vol 11 (14) ◽  
pp. 6550
Author(s):  
Doyun Jung ◽  
Wonjin Na
Keyword(s):  
Acoustic Emission ◽  
Irradiation Time ◽  
Tensile Load ◽  
Failure Behavior ◽  
Value Analysis ◽  
Emission Testing ◽  
Final Failure ◽  
Acoustic Emission Testing ◽  
Woven Glass Fiber ◽  
Ae Signals

The failure behavior of composites under ultraviolet (UV) irradiation was investigated by acoustic emission (AE) testing and Ib-value analysis. AE signals were acquired from woven glass fiber/epoxy specimens tested under tensile load. Cracks initiated earlier in UV-irradiated specimens, with a higher crack growth rate in comparison to the pristine specimen. In the UV-degraded specimen, a serrated fracture surface appeared due to surface hardening and damaged interfaces. All specimens displayed a linearly decreasing trend in Ib-values with an increasing irradiation time, reaching the same value at final failure even when the starting values were different.

Acoustic Emission Monitoring of Leaks in Pipes for Transport of Liquid and Gaseous Media: A Model Experiment

2006 ◽  
Vol 13-14 ◽  
pp. 351-356 ◽  
Author(s):  
Andreas J. Brunner ◽  
Michel Barbezat
Keyword(s):  
Acoustic Emission ◽  
Model Experiment ◽  
Structural Integrity ◽  
Fiber Composite ◽  
Leak Testing ◽  
Pipe Segment ◽  
Potential Applications ◽  
Gaseous Media ◽  
The Media ◽  
Ae Signals

In order to explore potential applications for Active Fiber Composite (AFC) elements made from piezoelectric fibers for structural integrity monitoring, a model experiment for leak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 60 mm has been operated with gaseous (compressed air) and liquid media (water) for a range of operating pressures (between about 5 and 8 bar). Artificial leaks of various sizes (diameter) have been introduced. In the preliminary experiments presented here, commercial Acoustic Emission (AE) sensors have been used instead of the AFC elements. AE sensors mounted on waveguides in three different locations have monitored the flow of the media with and without leaks. AE signals and AE waveforms have been recorded and analysed for media flow with pressures ranging from about 5 to about 8 bar. The experiments to date show distinct differences in the FFT spectra depending on whether a leak is present or not.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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