Acoustic Emission Signal Analysis by Wavelet Method to Investigate Damage Mechanisms During Drilling of Composite Materials

2010 ◽  
Author(s):  
Hossein Heidary ◽  
Amir Refahi Oskouei ◽  
Milad Hajikhani ◽  
Behrooz Moosaloo ◽  
Mehdi Ahmadi Najafabadi
Keyword(s):  
Acoustic Emission ◽  
Cutting Speed ◽  
Damage Mechanism ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Emission Analysis ◽  
Wavelet Method ◽  
Emission Signal ◽  
Composite Damage ◽  
Acoustic Emission Sensing

Structural parts made of composites have frequently to be drilled in the industry. However, little is now about the interacting conditions between the drill tool and material, which may be multi-type and multi-size. Delamination free in drilling different fiber reinforced composites is the main objective of present paper. Therefore the influence of drilling and materials variables thrust force and delamination of GFRP composite was investigated experimentally. Drilling variables are cutting speed and feed; material variable is fiber orientation. Acoustic Emission sensing was employed for online detection of composite damage induced by drilling. This paper addresses an application of wavelet-based signal processing technique on a composite during drilling. The wavelet methodology is introduced and procedure of wavelet-based acoustic emission (AE) analysis methods is demonstrated. Result shows Acoustic Emission analysis by wavelet method can monitor damage mechanism in drilling of composites.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Evaluation of acoustic emission burst detection methods in a gearbox under different operating conditions

2018 ◽  
Vol 25 (4) ◽  
pp. 895-906 ◽  
Author(s):  
F. Leaman ◽  
C. Niedringhaus ◽  
S. Hinderer ◽  
K. Nienhaus
Keyword(s):  
Acoustic Emission ◽  
Processing Technique ◽  
Operating Conditions ◽  
Detection Methods ◽  
Threshold Values ◽  
Burst Detection ◽  
Signal Processing Technique ◽  
Planetary Gearbox ◽  
Machine Condition Monitoring ◽  
Definition Of

In account of its abilities to follow the damage progression, also at early stages, the acoustic emission (AE) analysis has become an attractive technique for machine condition monitoring. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under analysis works under variable operating conditions, threshold-based methods could lead to poor results due to the influence of these conditions on the AE generation. The present work compares the ability of three AE burst detection methods in a planetary gearbox working under different rotational speeds and loads. The results showed that performance could be significantly improved by using factors of the root mean square value as threshold values instead of fixed values. Among the evaluated methods, the method that includes demodulation and differentiation as a signal processing technique had the best performance overall.

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

Material removal monitoring in precision cylindrical plunge grinding using acoustic emission signal

2013 ◽  
Vol 228 (4) ◽  
pp. 715-722 ◽  
Author(s):  
Chen Jiang ◽  
Haolin Li ◽  
Yunfei Mai ◽  
Debao Guo
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Material Removal ◽  
Grinding Force ◽  
Force Model ◽  
Precision Grinding ◽  
Emission Signal ◽  
Proposed Model ◽  
Acoustic Emission Sensing ◽  
Series Of Experiments

A mathematical model of the acoustic emission signal during a grinding cycle is proposed for the monitoring of material removal in precision cylindrical grinding. Acoustic emission signals generated during precision grinding are sensitive to forces in grinding and present opportunities in accurate and reliable process monitoring. The proposed model is developed on the basis of a traditional grinding force model. Using the developed model, a series of experiments were performed to demonstrate the effectiveness of the acoustic emission-sensing approach in estimating the time constant and material removal in grinding. Results indicate that acoustic emission measurements can be used in the prediction of material removal in precision grinding with excellent sensitivity.

Acoustic Emission Analysis of Fracture Events in Cu Films with W Overlayers

1999 ◽  
Vol 563 ◽  
Author(s):  
Alex A. Volinsky ◽  
William W. Gerberich
Keyword(s):  
Acoustic Emission ◽  
Energy Release ◽  
Strain Energy ◽  
Strain Energy Release ◽  
Crack Arrest ◽  
Work Of Adhesion ◽  
Emission Analysis ◽  
Cu Films ◽  
Plastic Energy ◽  
Emission Signal

AbstractIndentation-induced delamination of thin films provides the basis for adhesion calculations. In the case of ductile Cu films plastic deformation usually prevents a film from debonding from the substrate. Deadhesion is facilitated by the use of a hard W superlayer, which promotes indenter-induced Cu film failure, increasing the delamination area by an order of magnitude. Radial as well as annular cracking acts like a secondary mechanism in the strain energy release, and can be resolved from excursions on the load-displacement curves. For the thicker Cu films no excursions were observed, though radial cracking took place. It is important to identify fracture events as they occur in order to understand the system behavior and accurately apply the analysis. An acoustic emission signal is used to detect the magnitude of fracture events in thin Cu films. For the films of different thickness from 40 nm to 3 microns the corresponding interfacial fracture energy ranged from 0.2 to over 100 J/m2. Limits of plastic energy dissipation are determined with the lower limit, the true work of adhesion, being associated with a dislocation emission criterion. Crack arrest marks were found upon the blister removal, and are proposed to represent the shape of the crack tip. Total acoustic emission energy was found to be inversely proportional to the strain energy release rate.

scholarly journals Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials

2021 ◽  
Vol 11 (19) ◽  
pp. 8801
Author(s):  
Ekhard K. H. Salje ◽  
Xiang Jiang ◽  
Jack Eckstein ◽  
Lei Wang
Keyword(s):  
Acoustic Emission ◽  
Failure Process ◽  
Fast Response ◽  
Probability Density Distribution ◽  
Material Failure ◽  
Destructive Testing ◽  
Human Teeth ◽  
Emission Analysis ◽  
Emission Signal ◽  
Testing Technology

As a non-destructive testing technology with fast response and high resolution, acoustic emission is widely used in material monitoring. The material deforms under stress and releases elastic waves. The wave signals are received by piezoelectric sensors and converted into electrical signals for rapid storage and analysis. Although the acoustic emission signal is not the original stress signal inside the material, the typical statistical distributions of acoustic emission energy and waiting time between signals are not affected by signal conversion. In this review, we first introduce acoustic emission technology and its main parameters. Then, the relationship between the exponents of power law distributed AE signals and material failure state is reviewed. The change of distribution exponent reflects the transition of the material’s internal failure from a random and uncorrelated state to an interrelated state, and this change can act as an early warning of material failure. The failure process of materials is often not a single mechanism, and the interaction of multiple mechanisms can be reflected in the probability density distribution of the AE energy. A large number of examples, including acoustic emission analysis of biocemented geological materials, hydroxyapatite (human teeth), sandstone creep, granite, and sugar lumps are introduced. Finally, some supplementary discussions are made on the applicability of Båth’s law.

Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

2021 ◽  
Author(s):  
Chun-Wei Liu ◽  
Shiau-Cheng Shiu ◽  
Kai-Hung Yu
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Optical Glass ◽  
Removal Rate ◽  
Support Vector ◽  
Grinding Wheel ◽  
Emission Signal ◽  
Sensing Technology ◽  
Acoustic Emission Sensing ◽  
Circularity Error

Abstract A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of <E0.1, and an optical axis error of <150 μrad.

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