Papers by Keyword: Acoustic Emission (AE)

Abstract: In-situ evaluation of crack generation using acoustic emission (AE) technique as a non-destructive testing method was applied during laser cladding of WC-Co cemented carbide via a multi-beam laser metal deposition (LMD) system. Especially, this study aims to investigate relationships between the laser output and the number of cracks generated in beads of WC-12mass%Co cemented carbide cladded by the LMD system. The number of cracks was evaluated using an AE sensor attached to a substrate. The number of cracks was also evaluated from SEM images of beads. By comparing these results, problems in the both evaluation methods for crack generation in laser cladding were discussed.

Abstract: Additive manufacturing (AM) methods have become popular but the fracture mechanism of products made by AM is not well understood. In particular, the fracture of parts made by 3D printing needs more investigation. We have already investigated the effect of the lamination direction on the fractures in bearing specimens. In this study, we made some specimens by using a 3D printing method and performed some tensile tests. We investigated the effect of the lamination direction on the Young’s modulus of the specimens and tried to detect inner defect initiation using an acoustic emission (AE) sensor.

Abstract: This paper presents the results of the study of strength and fracture processes of FRP samples, obtained by vacuum and vacuum-autoclave molding methods. The experimental studies consisted of a three-point bending test with step loading, accompanied by an acoustic emission method. Based on the acoustic emission data recorded using the acoustic-emission software and hardware complex during the experiments, a method for estimating the accumulated damage using various techniques for analyzing acoustic emission parameters was tested. The results of methods for analyzing the power-law coefficient of accumulation of total AE, b-value and density distribution of amplitudes and median frequencies are considered. An estimate of the power-law coefficient of accumulation of the total AE made it possible to determine the bearing capacity of PCM samples. Using the techniques for analyzing the amplitude distribution of the AE signals and the distribution density of the amplitudes and median frequencies of the AE signals, destructive processes in the volume of samples were described and the stages of their damaging were revealed.

Abstract: The paper presents the results of the study of the stage of accumulation of damage and fatigue rupture of titanium alloys (using the method of acoustic emission). The main object of research was the development of a method for designing a generalized fatigue diagram characterizing the stage of fatigue damage accumulation. The studies aimed at experimental verification of the hypothesis of the stage of damage accumulation, which can be established only by the registered parameters of acoustic emission with separate analysis by types of acoustic emission sources. In contrast to the method of research, which is carried out fractographic analysis, the use of acoustic emission method can significantly reduce the amount of testing. The types of acoustic emission sources on the distribution plane of two-parameter “AE signal energy E_AE vs. frequency parameter K_f” are considered. Fatigue stages in the tests of trial alloys were determined by the activity of the AE signals emitted by different types of AE sources (dislocation, micro - and macro-cracks). A generalized diagram of fatigue developed according to the specified stages. The developed method significantly reduces the volume of fatigue tests and fractographic studies.

Abstract: This work is devoted to the investigation of the characteristics of acoustic emission waves to establish their relations with the parameters of the fracture of the structure of the material. The paper presents the results of the analysis of acoustic emission signals recorded during the propagation of ultrasonic waves in metal sheet materials using piezoelectric sensors. The specimen was a rectangular aluminum plate. The piezoelectric sensor recorded acoustic emission signals generated by the Hsu-Nielsen source. The piezoelectric sensor is located in the center of the aluminum plate. Then sources are generated with different hardness to model various kinds of cracks at each specific location. To determine the informative component of a useful acoustic emission signal, the Morlet wavelet transformation was used. When excited by a fracture pencils of different hardness, the magnitude of the wavelet differ in the energy and intensity of the spectrogram.

Abstract: Acoustic emission (AE) signals can provide tool condition that is critical to effective process control. However, how to process the data and extract useful information are challenging tasks. This paper presented an intelligent grinding wheel wear monitoring system which was embedded in a surface grinding machine. An AE sensor was used to collect the grinding signals. The grinding wheel wear condition features were extracted by a proposed novel method based on statistics analysis of the average wavelet decomposition coefficient. The detailed signal characteristics during different wear condition are described. A BP neural network was used to classify the conditions of the grinding wheel wear. The inputs of the neural network were the three extracted features, and the outputs were three different states of grinding wheel condition, namely primary wear, intermediate wear and serious wear. The intelligent monitoring system was evaluated through grinding experiments. The results indicate that the effectiveness of the proposed method for extracting features of AE signals and developed intelligent grinding wheel wear monitoring system are satisfied.

Abstract: Precision grinding is generally used as the final finishing process, and it determines the surface quality of the machined component. It’s very difficult to achieve on-line measurement of the surface roughness. The purpose of this research was to study the surface roughness prediction and avoid the defect happening in the grinding process. A surface roughness prediction model was proposed in this paper, which presented the relationship between surface roughness and the wear condition of grinding wheel and grinding parameters. An AE sensor was used to collect the grinding signals during the grinding process to obtain the grinding wheel wear condition. Besides, a fuzzy neural network was used to obtain the prediction surface roughness. Grinding trials were performed on a high precision CNC cylindrical grinder (MGK1420) to evaluate the surface roughness prediction model. Experiment verified that the developed prediction system was feasible and had high prediction accuracy.

Abstract: Under the influence of the pressure caused by the application of the cutting edge on the surface of the workpiece elastic waves are generated. Waves propagate in the material in every possible direction and can be identify by specialized measuring equipment. Acoustic emission phenomenon was used to determine the beginning of decohesion process. The article presents a new method for determination of the decohesion process during peripheral milling performed with the indexable cutting tools on samples made out of titanium based alloy and nickel based alloy.

Abstract: This paper investigates the fracture initiation at the vicinity of a micro-groove caused by thermal stress through computational and experimental studies. Fracture initiation time has been determined by analyzing the stress intensity factor. Stress distribution was examined by using a finite element method, and the fracture toughness of the material was determined by using a Vickers indentation technique. The fracture initiation process was verified experimentally by monitoring the acoustic emission signals, while the temperature of the laser spot was also measured using two-color pyrometer. The result shows that stress intensity factor was increased with the increase in micro-groove sizes. Consequently, fracture initiation was attained faster. The simulation results show a good agreement with the experimental results in examining the fracture initiation and recognizing the ideal micro-groove parameters for laser cleaving process.

Abstract: In this paper, in order to strengthen the prediction of rockburst and inquire the relationship between the rockburst proneness of rock and its AE characteristic, each kind of typical rock of volcanic, sedimentary and metamorphic were selected to doing indoor rock mechanics experiments. Calculate the rockburst proneness of these rocks by uniaxial compression tests. In uniaxial compression, we collect the entire process of AE signals by using acoustic emission instrument, then we analysis the AE characteristics of different rocks to inquire the relationship between the AE characteristic and its rockburst proneness.