Papers by Keyword: Acoustic Emission (AE)

Abstract: – Welding defects can create a major threat in industrial equipments and pressure vessels can during operation. Detecting and identifying of existing welding defects has a great role in the assessment of the probability of failure in different situations. Acoustic emission (AE) is only a nondestructive technique that can be used as in-service testing and used on large structures. This paper examines the changes in the properties of AE source generated from Pencil Lead Break (PLB). AE is recorded across different seam welds with pre-identified welding defects. Series of experiments are carried out on three certified standard Carbon Steel plates. Each plate has different pre-identified defect such as lack of fusion, porosity and slag. Different AE parameters such Energy, Amplitude and number of counts have been used to identify quantitatively the type of defects. The results are showing that AE technique is capable to identifying the different defects types which can be upgraded for higher productivity and accuracy for welding inspection.

Abstract: The acoustic emission (AE) technique is used for investigating the interfacial fracture and damage propagation in GFRP-and SRG-strengthened bricks during debonding tests. The bond behavior is investigated through single-lap shear bond tests and the fracture progress during the tests is recorded by means of AE sensors. The fracture progress and active debonding mechanisms are characterized in both specimen types with the aim of AE outputs. Moreover, a clear distinction between the AE outputs of specimens with different failure modes, in both SRG-and GFRP-strengthened specimens, is found which allows characterizing the debonding failure mode based on acoustic emission data.

Abstract: New nanomaterials such as carbon nanotubes and nanofibres considerably improve performance of current building materials and they can contribute to new application facilities. Alkali activated slag is a material having a great potential to be used in practice. The main drawback of this material is a high level of autogenous and especially drying shrinkage, which causes a deterioration of the mechanical fracture properties. The aim of this paper is introduce the effect of carbon nanotubes admixture on the microstucture and mechanical performance of alkali activated slag mortars. The three-point bending tests of specimens with central edge notch were performed. Method of acoustic emission was used during this testing.

Abstract: This paper reports the analysis of acoustic emission signals captured during three-point bending fracture test of specimens of concrete. Much has been said in literature about the fracture energy of concrete and its importance. Acoustic emission is an experimental tool well suited for monitoring fracture processes. Quantitative acoustic emission techniques were used to measure micro fracture properties. For three different concrete mixtures typical acoustic emission patterns were identified in the acoustic emission records to further describe the under-the-stress behaviour and failure development. An understanding of microstructure–performance relationships is the key to true understanding of material behaviours. The results obtained in the laboratory are useful to understand the various stages of micro-cracking activity during the fracture process in quasi-brittle materials such as concrete and extend them for field applications.

Abstract: It is known that concrete is one of the most popular building materials nowadays. There are many traditional, mainly destructive methods for monitoring its properties during its lifetime. It is clear that its properties depend significantly on the composition of concrete mixture. Concrete lifetime depends mainly on curing during the first 7 days after making the mixture. The paper shows the possibilities of applying Acoustic Emission and Impedance Spectroscopy methods during the early age of concrete.

Abstract: As a non-traditional, non-destructive analysis method, the Acoustic Emission Method was used for civil engineering experiment. Concrete is a composite construction material frequently used in civil engineering. We know that concrete is like a man – when concrete is made it is like a baby, then it ages and its properties change in accordance to its baby life. That means it is better to monitor and change its properties when it is young as soon as possible. However, using methods immediately after concrete birth (making the mixture) is difficult. The main aim of the article is to show the application of Acoustic Emission Method during concrete lifetime, particularly during the first days after mixing the mixture. Article describes monitoring concrete structure for 20 days since their making up. Primary interesting part is during first day of hardening. Acoustic Emission Method has used for monitoring both samples. Expected result has been between covered and uncovered specimen.

Abstract: As an important part of pipeline safety, pipeline leak detection is usually done by extracting feature vectors of leakage signal. Many researchers used wavelet packet algorithm to extract feature vector, but because of mixing effects of wavelet packet, the acquired feature vector may be not accurate. To solve this problem, the authors propose an improved wavelet packet algorithm to extract the feature vector. The improved algorithm is different from the traditional algorithm in decomposition and reconstruction and the feature vector is constituted by three time-frequency domain parameters. A lot of experiments have been performed to extract feature vector based on the proposed algorithm, with the results showing that the proposed algorithm can overcome the mixing effects and accurately extract the feature vector.

Abstract: This paper presents synthesis of acoustic-emission (AE) wave propagation in multi-layer materials and simulation of AE wave responses at free surface. In particular, the AE source is modelled as an arbitrary-orientation dislocation over an inclined-to-surface fault within one layer or at the layer-to-layer interface, while the materials are assumed as multi-layer media, each of which is homogeneous, isotropic and linearly elastic. With the use of the integral transformation approach, the three-dimensional wave propagation in the materials is solved in transformed or frequency-wavenumber domain. Subsequently, a closed-form solution for wave responses at free surface is found, which can then be converted in time-space domain. Numerical examples are finally provided for illustration.

Abstract: In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AE_RMS show uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AE_RMS and specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

Abstract: In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AE_RMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.