Advanced Materials Research Vols. 13-14

Abstract: The author has been involved in acoustic emission technology (AET) for over forty years, since 1962. Activity in AET for these many years has provided the author with a unique insight into a technology that has seen many successes and also many failures. Much anecdotal material has been circulated. The author separates legend from fact and shares some little known anecdotes of his own.

Abstract: This paper reviews various approach used in acoustic emission (AE) testing of structures so that further improvements can be realized in this important application of AE technology. In the past half century, many successful AE tests of structures have been completed, but quite often details remained private. Here, we attempt to organize the AE methodology in four steps. 1. Primary sources of brittle fracture, micro or macro cracks in contrast to secondary sources of friction or fretting, rust, etc. 2. Kaiser effects, arising from the irreversibility of AE, allow the detection of prior loading level and of damage states. 3. Source Location: This approach identifies the area of integrity loss. 4. Source Characterization: Combined AE parameters give good insight to the flaw types, but many other methods, including attenuation-corrected signal amplitude, signal frequency, waveform and wave propagation analysis and moment tensor analysis, may be useful. Avenues for better AE technology are suggested.

Abstract: Acoustic emission (AE) testing is an increasingly popular technique used for nondestructive evaluation (NDE). It has been used to detect and locate defects such as fatigue cracks in real structures. The monitoring of fatigue cracks in plate-like structures is critical for aerospace industries. Much research has been conducted to characterize and provide quantitative understanding of the source of emission on small specimens. It is difficult to extend these results to real structures as most of the experiments are restricted by the geometric effects from the specimens. The aim of this work is to provide a characterization of elastic waves emanating from fatigue cracks in plate-like structures. Fatigue crack growth is initiated in large 6082 T6 aluminium alloy plate specimens subjected to fatigue loading in the laboratory. A large specimen is utilized to eliminate multiple reflections from edges. The signals were recorded using both resonant and nonresonant transducers attached to the surface of the alloy specimens. The distances between the damage feature and sensors are located far enough apart in order to obtain good separation of guided-wave modes. Large numbers of AE signals are detected with active fatigue crack propagation during the experiment. Analysis of experimental results from multiple crack growth events are used to characterize the elastic waves. Experimental results are compared with finite element predictions to examine the mechanism of AE generation at the crack tip.

Abstract: Acoustic emission (AE) monitoring was performed on an aluminium landing gear component that was undergoing testing to investigate its fracture resilience. The type of component was identified from FE analysis and previous fatigue testing. The component was loaded in fatigue for 500 flight cycles before re-greasing of the bearings. After 2,000 cycles the component was removed for NDT inspection. The AE investigations were implemented after 83,000 flight cycles had been completed. NDT at this point had shown that the component contained no damage. This paper presents the findings of the final 2,000 cycles monitored. The AE investigation detected and located, using both linear and planar location approaches, one region of activity around the grease pin. Fretting damage at this location was confirmed using dye pentrant testing. It was also shown that the increase in rate of detected activity is a significant tool in the identification of damage in landing gear components.

Abstract: The paper describes a methodology for the reliable detection of incipient damage due to fatigue, fretting and false brinelling in large, heavily loaded rolling element bearings such as found in pedestal slewing cranes and ship azi-pod propulsors. It has been found that combining acoustic emission source location and spectrum analysis of the associated time-domain signatures has produced a powerful diagnostic tool for the detection of micro-damage to the various working faces of the bearing under variable speed and loading conditions, before any metal loss is evident in the bearing lubricant. Other sources of acoustic emission such as fretting at contact faces elsewhere in the body of the bearing and fluid turbulence can be resolved and quantified so as not to interfere with the diagnosis of bearing condition. Results are presented for new and damaged bearings, where the true condition has been verified when the bearings were subsequently replaced.

Abstract: The use of AE by maintenance personnel for monitoring the condition of rotating machinery on the industrial shop floor is now well established and provides both a quick and effective assessment. Despite early resistance, especially by those accustomed to vibration based monitoring, it now enjoys a widespread acceptance. The development of signal processing routines and instrumentation specifically for the condition monitoring role has been a major factor in this achievement. Experience has shown that as a portable instrument AE can be very quickly applied and give instant indications of machine condition with high sensitivity to fault conditions. Appropriately pre-processed AE signals are particularly useful for on-line monitoring since the fault indications are in general less affected by changes in operating conditions than vibration based techniques as well as being far simpler to interpret. This is especially important where many machines are being simultaneously monitored. This paper discusses the accompanying developments and presents illustrative application examples.

Abstract: This paper presents the findings of an investigation to determine theoretically and empirically the wave speeds and frequency content of the two primary Lamb wave modes, the symmetric (S0) and anti-symmetric (A0). A 2 mm thick steel plate measuring 700 mm by 700 mm was used to perform all measurements. A broadband pulse propagated through the plate and detected by a conical type piezoelectric receiver was used to show how the dispersive properties of the plate influenced the detected AE signals. It was shown that the two primary Lamb wave modes cover a very broad range of velocities, leading to a severe spreading of arrival times. A further investigation was completed using four acoustic emission sensors to record a pencil lead fracture, which was used as an artificial source. Reflections in the plate were shown to cause interference in the signal that can complicate the interpretation of the arrival modes. A recorded signal 400mm from the source was filtered into frequency bands. The arrival times of the wave modes were determined for each frequency band and the appropriate velocities calculated allowing a dispersion curve to be plotted experimentally. The plotted curve was shown to be a very close approximate to the calculated curve.

Abstract: Acoustic emission (AE) practitioners routinely use surface pencil lead breaks (monopoles) to observe expected AE signal characteristics. In contrast, stress-generated AE sources are almost universally composed of dipoles. Thus, understanding the primary differences between the signals generated by these two different source classes is of key importance. This research had the goal of analyzing and contrasting the AE signals generated by monopole and dipole sources. A finite-element-modeled database of AE signals provided an ideal means to study these two source types. The AE signals represented the top-surface out-of-plane displacement versus time from point sources inside an aluminum plate 4.7 mm thick. In addition, monopole sources both on the plate top surface and the edge surface were included in the database. The AE signals were obtained from both in-plane and out-of-plane monopole and dipole sources. Results were analyzed with both a 100 to 300 kHz bandpass filter and a 40 kHz high-pass filter. The wide-plate specimen domain effectively eliminated edge reflections from interfering with the direct signal arrivals.

Abstract: Acoustic emission (AE) techniques have obvious attractions for structural health monitoring (SHM) due to their extreme sensitivity and low sensor density requirement. A factor preventing the adoption of AE monitoring techniques in certain industrial sectors is the lack of a quantitative deterministic model of the AE process. In this paper, the development of a modular AE model is described that can be used to predict the received time-domain waveform at a sensor as a result of an AE event elsewhere in the structure. The model is based around guided waves since this is how AE signals propagate in many structures of interest. Separate modules within the model describe (a) the radiation pattern of guided wave modes at the source, (b) the propagation and attenuation of guided waves through the structure, (c) the interaction of guided waves with structural features and (d) the detection of guided waves with a transducer of finite spatial aperture and frequency response. The model is implemented in the frequency domain with each element formulated as a transfer function. Analytic solutions are used where possible; however, by virtue of its modular architecture it is straightforward to include numerical data obtained either experimentally or through finite element analysis (FEA) at any stage in the model. The paper will also show how the model can used, for example, to produce probability of detection (POD) data for an AE testing configuration.

Abstract: Good knowledge of acoustic emission (AE) source location is the basic requirement for further damage mechanism characterization. Calculation of the AE source location is mostly based on arrival time differences of the signals recorded by different transducers. Error free arrival time determination is the crucial factor for the localization results accuracy together with the exact elastic wave velocity measurement. In the paper difficulties and limitations of the elastic wave velocity computation are shown. To solve the velocity and the time differences problems, new approach to AE source localization is described. The new method estimates the AE source coordinates using artificial neural network (ANN) processing extracted signal parameters. The ANN do not uses neither arrival time differences nor elastic wave velocities as input data. The new approach advantages are discussed in cases of both numerical and practical experiments. The experiments results are promising for the use of designed localization method in praxis.