Characterisation of fiber failure mode in T-700 carbon fiber reinforced epoxy composites by acoustic emission testing

Acoustic emissions generated by a structure under stressed condition provide an insight in to the dynamic behaviour of flaws in the structure for characterization of failure modes. Fiber failure mechanism in T-700 carbon epoxy composites is characterized by testing unidirectional specimens in longitudinal mode. Acoustic emission parameters like amplitude, energy, duration, and signal strength have been recorded and studied with respect to the applied load to assess the fiber failure characteristics. The AE data is analyzed with different correlation plots for visual pattern recognition. Significant fiber breakage is observed at above 70% of the load. Bi-linear trend of the cumulative amplitude distribution curve indicates distinctively matrix and fiber failures. Matrix cracking failure mechanism dominated the entire loading cycle and is represented by AE hits of up to 85 to 90 dB amplitude and the peak amplitude distribution is 58 to 75 dB. The wave forms of matrix cracking hits with less than 90 dB and 100 units of energy are having up to 273 kHz frequency with a peak around 100 kHz. The wave forms of fiber breakage hits with more than 90 dB and 100 units of energy have up to 448 kHz frequency and with a peak from 168 to 437 kHz. From the low amplitude filtering technique the border line for fiber breakage is observed from 89 to 92 dB.     

Particle monitoring method using acoustic emission signal for analysis of slider/disk/particle interaction

One of the most important reliability issues in an information storage device is the contamination problem. The slider and disk can be damaged by the particles intruded into the slider/disk interface (SDI). In this work, in order to monitor the slider/disk interaction due to particle injection the acoustic emission (AE) method, which is typically utilized for the detection of slider contact, was used. The raw as well as frequency spectrum of the AE signal were obtained during the particle injection test. The particles were artificially injected inside the test apparatus to simulate the effect of contamination on the slider/disk interaction. SiC and polystyrene particles were used for the tests. As a result, the 1st torsional and bending mode frequencies of the nano-slider were observed when 1 μm SiC particles and 60 nm polystyrene particles were injected into the SDI. Also, it was shown that the particle behavior at the SDI can be predicted from the characteristics of the AE raw signal.     

Artificial neural network a tool for predicting failure strength of composite tensile coupons using acoustic emission technique

A series of 18 tensile coupons were monitored with an acoustic emission (AE) system, while loading them up to failure. AE signals emitted due to different failure modes in tensile coupons were recorded. Amplitude, duration, energy, counts, etc., are the effective parameters to classify the different failure modes in composites, viz., matrix crazing, fiber cut, and delamination, with several subcategories such as matrix splitting, fiber/matrix debonding, fiber pullout, etc. Back propagation neural network was generated to predict the failure load of tensile specimens. Three different networks were developed with the amplitude distribution data of AE collected up to 30%, 40%, and 50% of the failure loads, respectively. Amplitude frequencies of 12 specimens in the training set and the corresponding failure loads were used to train the network. Only amplitude frequencies of six remaining specimens were given as input to get the output failure load from the trained network. The results of three independent networks were compared, and we found that the network trained with more data was having better prediction performance.     

A criterion for evaluating the brittle fracturing of glass using acoustic emission signals

Some features of the fracturing of silicate glass materials are considered. The brittle fracturing of glasses is shown to begin under small applied forces that are characterized by low-amplitude acoustic emission (AE) signals. The further propagation of a fracture is shown to be accompanied by a change in the amplitude-frequency characteristics of AE signals, which we propose to describe with a continuous wavelet transform. A criterion for estimating the brittle fracture of glass with the use of attendant AE signals is proposed.     

The role of shape memory alloy on impact response of glass/epoxy laminates under low temperature

The paper aims to evaluate the impact response of glass/epoxy laminates with embedded shape memory alloy (SMA) subject to low velocity impact at various temperatures. For the goal, the impact tests were performed by using an instrumented impact-testing machine at three temperatures: 293K, 263K and 233K for the baseline (laminates without SMA wires) and SMA laminates (laminates with embedded SMA wires). And the resultant damages were inspected through the scanning acoustic microscope (SAM). Also, based on the impact force history and the damage configuration, the impact resistance parameters were employed to evaluate damage resistance of laminates with embedded SMA wires. As a result, it was observed that the damage resistance of glass/epoxy laminates is influenced by embedded SMA wires and embedding SMA wires into laminates does not compromise the structure any differently to laminates without wires. In fact, it has been shown that under lower temperature, the SMA laminates have a little superior damage resistance compared with the baseline laminates.     

Detecting transition to chatter mode in peakless tool turning by monitoring vibration and acoustic emission signals

Stability of a peakless tool turning on slender shafts was studied under conditions of low- and high-magnitude vibrations by registering and short-time Fourier transformation (STFT) processing of acoustic emission (AE) and vibration acceleration (VA) signals. Both VA and AE signals have been registered in three positions of the cutting tool on the workpiece and for different shaft diameters. Both amplitude- and frequency-dependent AE and VA characteristics were obtained and analyzed for overall process signal length as well as for single frames. It was shown that power spectrum characteristic could be used for monitoring the fast-occurring changes in the cutting process stability. A criterion of the cutting process stability based on the power spectrum has been offered.     

Process monitoring in precision cylindrical traverse grinding of slender bar using acoustic emission technology

The precision cylindrical traverse grinding process of slender bar is very complex for the strongly time dependent properties of the wheel. Therefore, it is very difficult for operators to properly judge the grinding state using naked eyes and ears. This calls for automatic monitoring technology that can monitor the process in precision cylindrical traverse grinding to guarantee machining quality and productivity as well as reduction in cost. This study developed an automatic monitoring system for precision cylindrical traverse grinding of slender bar using Acoustic emission (AE) technology. Grinding tests on molybdenum were conducted under traverse conditions in a conventional cylindrical grinder. It was found that larger radial material removal depth results in larger root mean square value of Acoustic emission signals (AE_RMS). Based on this, the AE_RMS was analyzed and used to determine the finishing of spark-out process and the pre-processing of tool alignment. The variation tendency of AE_RMS in one spark-out process was applied to determine when a wheel wears out and has to be dressed. The experimental results showed that the AE system was effective to monitor the pre-processing of tool alignment, spark-out and wheel wear in precision cylindrical traverse grinding of slender bar.

     

Online monitoring of precision optics grinding using acoustic emission based on support vector machine

This paper aims to accomplish online monitoring of precision optics grinding with processing condition factors based on theoretical analysis and through grinding experiments. The model for monitoring surface quality of optical elements online (OSQMM) which contains identification model (IM) and interpolation·factor-support vector regression (i?f-SVR) is proposed. IM is applied to analyze and determine which kind of processing condition factors and which kind of its feature parameters are the best one to be used for online monitoring. i?f-SVR which contains the effect factor (fe) and interpolation function (I) to overcome the drawbacks of existing SVR models is applied to predict the monitoring thresholds. The grinding experiments were designed and performed. The influences of technological parameters (e.g., grain size of the grinding wheel, grinding depth, speed of the grinding wheel, speed of the worktable, and materials of workpiece) and processing condition factors (e.g., acoustic emission, grinding force, and vibration) on the surface quality were investigated and analyzed by IM. i?f-SVR was trained and established by the data which were gained through the experiments. After that, the other grinding experiments were performed to apply and verify OSQMM. The results were that the accuracy of alarm for roughness was 85.19 % and the accuracy of alarm for surface shape peak–valley value was 75.93 %. The results show that this method can be effectively applied to monitor the precision optics grinding process online.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Detection of faults in gearboxes using acoustic emission signal

Vibration analysis is widely used in machinery diagnosis, and wavelet transform and envelope analysis have also been implemented in many applications to monitor machinery condition. Envelope analysis is well known as a useful tool for the detection of rolling element bearing faults, and wavelet transform is used in research to detect faults in gearboxes. These are applied for the development of the condition monitoring system for early detection of the faults generated in several key components of machinery. Early detection of the faults is a very important factor for condition monitoring and a basic component to extend CBM (Condition-Based Maintenance) to PM (Prediction Maintenance). The AE (acoustic emission) sensor has a specific characteristic on the high sensitivity of the signal, high frequency and low energy. Recently, AE technique has been applied in some studies for the early detection of machine fault. In this paper, a signal processing method for AE signal by envelope analysis with discrete wavelet transforms is proposed. Through the 15 days test using AE sensor, misalignment and bearing faults were observed and early fault stage was detected. Also, in order to find the advantage of the proposed signal processing method, the result was compared to that of the traditional envelope analysis and the accelerometer signal.     

Damage detection in glass/epoxy composite structure using 8–12 GHz X-band

In this study, an electromagnetic wave 8–12 GHz X band microwave was utilized to detect various types of damage in a woven fabric composite structure. Damages, such as cracks, delamination, bubbles, and voids, were synthesized artificially in accordance to ASTM D2734. Variable thicknesses of composite structure were also analyzed, and the influence of defect on the transmitted signal was investigated. Network analyzer (ENA5701C) in X-band was utilized for this investigation. Detection was based on the changes in the electromagnetic properties, such as permittivity and permeability, and the reflection and transmission to microwaves were based on standard samples. These were implemented for each case of defect selected for this study. Experimental tests revealed that damage in the transparent glass/epoxy composite can be recognized clearly. Particularly, sample thickness was detected when the microwave was applied. This testing method can be considered an inline operation and non-destructive testing for such composites, especially during fabrication.     

Determination of particle impacts and impact energy in the erosion of X65 carbon steel using acoustic emission technique

An in-situ acoustic emission (AE) monitoring technique has been implemented in a submerged jet impingement (SIJ) system in an effort to investigate the effect of sand particle impact on the degradation mechanism of X65 carbon steel pipeline material in erosion conditions.A detailed analysis of the acoustic events' count rate enabled the number of impacts per second to be quantified for a range of flow velocities (7, 10, 15 m/s) and solid loadings (0, 50, 200, 500 mg/L) in a nitrogen-saturated solution at 50 °C. The number of impacts obtained from acoustic signals showed a strong agreement with theoretical prediction for flow velocities 7 and 10 m/s. A deviation between practical readings and theory is observed for flow velocity of 15 m/s which may be due to error from detected emissions of multiple rebounded particles.Computational fluid dynamics (CFD) was used in conjunction with particle tracking to model the impingement system and predict the velocity and impact angle distribution on the surface of the sample. Data was used to predict the kinetic energy of the impacts and was correlated with the measured AE energy and material loss from gravimetric analysis. The results demonstrate that AE is a useful technique for quantifying and predicting the erosion damage of X65 pipeline material in an erosion–corrosion environment.     

Analysis of the acoustic emission technique used in laboratory tests of reinforced-concrete structures

Deformation, crack generation, and fracture of reinforced-concrete structures are studied experimentally. Acoustic emission is used as one of the testing techniques to monitor the development of these processes. The performed experimental studies confirm the possibility of using the acousticemission technique to study the accumulation kinetics of flaws in reinforced-concrete structures and theoretically predicting fracturing when it is still difficult to do so on the basis of macroscopic observations. It is established that laboratory acoustic-emission tests of reinforced-concrete samples provide the necessary experimental basis for developing the theory of the deformation and fracture of reinforced concrete.     

A study of the brush/rotor interface of a homopolar motor using acoustic emission

Acoustic emission transducers were used to study the brush/rotor interface of a homopolar motor as a function of design parameters such as speed and load. The results indicate that acoustic emission is well suited for in situ monitoring of brush wear and rotor conditions, and for analyzing the tribological phenomena occurring at the brush/rotor interface.     

RETRACTED ARTICLE: Development and application of intensified envelope analysis for the condition monitoring system using acoustic emission signal

The application of the high-frequency acoustic-emission (AE) technique in the condition monitoring of rotating machinery has been increasing of late. It has a major drawback, though, the attenuation of the signal, and as such, the AE sensor has to be close to its source. Two signal-processing methods, envelope analysis and wavelet transform, were found to be useful for detecting faults in the rolling element bearing and gearboxes. These methods have a disadvantage, though: their application is focused only on a component of the assembled machine. For example, envelope analysis is a powerful method for detecting faults in the bearing system, but it is not proper for use in the gear system. Thus, these methods could not be used to detect combined faults in the common assembled machines. Therefore, we propose a signal-processing method consisting of envelope analysis and DWT (discrete wavelet transform). In addition, a novel mother function optimized for the AE signal for DWT was extracted through a fatigue crack growth test, and is also proposed herein. Then the proposed method, called intensified envelope analysis (IEA), was used to detect the faults in the rolling element bearing and rotating shaft. According to the results, IEA can be a better signal processing method for the condition monitoring system using AE technique.     

Application of acoustic emission technique for the detection of defects in rolling element bearings

The usefulness of acoustic emission (AE) measurements for the detection of defects in roller bearings has been investigated in the present study. Defects were simulated in the roller and inner race of the bearings by the spark erosion method. AE of bearings without defect and with defects of different sizes has been measured. For small defect sizes, ringdown counts of AE signal has been found to be a very good parameter for the detection of defects both in the inner race and roller of the bearings tested. However, the counts stopped increasing after a certain defect size. Distributions of events by ringdown counts and peak amplitudes are also found to be good indicators of bearing defect detection. With a defect on a bearing element, the distributions of events tend to be over a wider range of peak amplitudes and counts.     

Classification of acoustic emission signals collected during tensile tests on unidirectional ultra high molecular weight polypropylene fiber reinforced epoxy composites using principal component analysis

The objective of this study is to utilize a mathematical procedure for the evaluation of damage mechanism in ultra high molecular weight polypropylene/epoxy composite (UHMWPP/epoxy) specimen with different configuration based on the analysis of the AE signals of presented. Principal component analysis (PCA) is powerful tool, which utilized for the classification of the monitored AE transients. We worked on epoxy L160 resin, UHMWPP fiber bundle and UHMWPP/epoxy unidirectional specimens, subjected to tensile loading. Using model specimens exhibiting a predominant damage mechanism, correlations were established between the observed damage growth mechanisms and the AE parameter variances result in PCA. Outputs from this study revealed that the PCA is an effective tool for identifying damage modes such as matrix cracking, fibre/matrix debonding, fibre breakage and fibre pull-out in the UHMWPP/epoxy composites. The presence of damage modes in UHMWPP/epoxy composites was proven with scanning electron microscopy (SEM) images.     

快速博立叶变换红外光谱仪和气相色谱/表面声波分析技术在应急监测中的应用

介绍了快速傅立叶变换红外(FTIR)光谱仪和快速气相色谱/表面声波(GC/SAW)分析技术的工作原理;结合Gasmet DX4015 FTIR和zNOSE 4200 GC/SAW等仪器,讨论了两种方法在现场环境应急响应快速监测的实际应用;指出了两种方法发展方向以及各自最适宜的应用领域.     

A study of the tribological behaviour of piston ring/cylinder liner interaction in diesel engines using acoustic emission

The novel use of non-intrusive acoustic emission (AE) measurements to provide information pertaining to the interaction between piston rings and cylinder liners in a range of diesel engines is investigated in this paper. In doing so, this technique is shown to offer a new method of investigation into this important interface in engine operation.AE generated during normal engine operation is known to consist of contributions from a number of different sources such as injector and valve activity. A recent finding has been the identification of AE signals associated with the ring/liner interface which presents the opportunity for in-service monitoring. This work discusses the possible AE source mechanisms, such as asperity contact, lubricant flow and/or blowby, through reference to a number of tests on motored and in-service small HSDI diesel engines and large, 2-stroke, marine diesel engines. The influence of various factors such as engine speed, load and lubrication is considered.