Papers by Keyword: Acoustic Emission (AE) Signal

Abstract: This study deals with the micro v-grooving of a single crystal diamond tool that is implemented on a 3-axis micro-stage. A method for monitoring the machining conditions is investigated using acoustic emission (AE) signals and cutting force signals in the micro-grooving. The AE signals and machined surface profiles are obtained under various machining conditions. The signals are acquired from an AE sensor that is attached to the tool holder and are investigated to identify the correlation with the machined surface profiles. It is found that the AE signal is an effective parameter for monitoring the texture of the machined surface.

Abstract: Wigner-Ville distribution (WVD) has the characteristics of very high-energy accumulation and excellent time-frequency resolution. It is a good way to extract fault feature of acoustic emission (AE) signals due to mechanical component broken. The characteristics of typical AE signals initiated by damages are analyzed. Based on the extracting principle of AE signals from damaged components, the WVD analysis method of AE signal is developed. WVD method is employed to the fault diagnosis of rolling bearings with AE technique. The fault features reading from experimental data analysis are clear, accurate and intuitionistic, meantime, the validity and accuracy of WVD method proposed are nice from the experimental results. Therefore, WVD method is useful for condition monitoring and fault diagnosis in conjunction with AE technique.

Abstract: Aiming at inaccurately and inefficiently fault feature of early crack by the vibration method in the environment of strong noise, the acoustic emission signal (AE) is used to cracks defect with the advantages of sensitive. The Pseudo Wigner-Ville Distribution (PWVD) is introduced to extract the amplitude and frequency of AE signal as feature vector, which combines with support vector machine (SVM) to achieve prediction and diagnosis of fault types of different rotor cracks depth. It is shown by experiment that the proposed method have the features of obvious frequency characteristic, early prediction of fault time, accurate and reliable diagnosis results of early cracks fault diagnosis.

Abstract: Using BP Neural Network to optimize AE characteristic parameters of crack in drawing parts.By detecting the optimized characteristic parameters of crack, the crack in drawing parts are identified.According to the quality of drawing parts,the output of the network are crack signal and normal signal.Comparing the sensitivity of the input characteristic parameters on the output characteristic parameters,then pick the characteristic parameters which have bigger sensitivity values.Finally,the AE characteristic parameters such as Rise Time、AE Event Counter、Energy、Amplitude、Frequency Centroid can represent the signal of crack in the drawing parts better.These five characteristic parameters can identify the crack signal in the forming process of the drawing parts.

Abstract: The aim of this work is to analyse the mechanical response of the masonry specimens under long-term action by means of cyclic tests. To this end laboratory tests were carried out at the Non-Destructive Testing Laboratory of the Politecnico di Torino. The Acoustic Emission technique was employed to assess the damage evolution, and the mechanical properties decay in order to evaluate the extent and the evolution of micro and macro-cracking due to cyclic action until structural collapse in masonry blocks and mortar layers.

Abstract: The aim of this work is to develop a procedure to follow the evolution of micro and macro-crack in unreinforced and reinforced masonry walls by means of Acoustic Emission signal analysis. In particular the main goal of this experimental work want to be the definition of a characteristic signal parameter able to represents the evolution of the fracture dimension in the masonry walls during the fracture process. To this end laboratory tests were carried out at the Non-Destructive Testing Laboratory of the Politecnico di Torino.

Abstract: Ultrasonic machining (USM) is a mechanical material removal process used to erode holes and cavities in hard or brittle work pieces by using shaped tools, high-frequency mechanical motion, and an abrasive slurry. There are many physical signals in the ultrasonic machining which are related with the material removal rate, machining accuracy, and surface finish. So how to measure and control these signals with accuracy is very important. The aim of the paper is to summarize different kinds of physical signals and the methods of measuring and controlling them in ultrasonic machining.

Abstract: Engineering ceramics are hard and brittle materials, that is very easy to crack when grinding, and processing mechanism is rather complex than that of metallic materials. One kind of AE signal based grinding contact detection and feeding control method for ceramic material processing is presented in this paper based on ceramic grinding mechanism study. Through a large number of experimental data analyzing, the relationship between grinding touch and the feature of AE signals is obtained, and the automatic and intelligent grinding process can be realized by grinding contact signal driving grinding numeral program. Application results indicate this method can prove grinding efficiency and process quality.

Abstract: The purpose of this study is to find out the AE characteristics and fracture mechanisms through AE signal analysis for the weldment, PWHT specimen and basemetal of the pressure vessel steel. Charpy sized specimens were taken from the multi-passed weld block. Specimens were given to four point bend and AE tests. Lots of AE signals were emitted from the weldment compared with the basemetal and PWHT specimen. Besides, amplitude for the weldment was the largest, followed by PWHT specimen and basemetal and more AE counts for the weldment were emitted in the process of deformation. Lots of microcracks around the notch for the weldment were formed so that more AE signals were produced. In addition, second phase particle such as MgO for the basemetal acts as AE source. However, in case of weldment, debonding mechanisms between matrix and hard oxides which are formed during welding in air attributed to the emission of AE signals and softened particles for the PWHT specimen cause to produce the low level AE signals.

Abstract: A web based remote monitor & control system for grinding process is presented. In this system, OPC/Socket drive technology, CAN bus technology, B/S structure, Java Applet and ActiveX etc. are used to realize the remote monitor & control, and grinding processes in different place can be monitored and controlled by remote grinding expert only through browser, so it overcomes the shortage that the grinding quality depends on skilled workers and variants with grinding operators. The data processing and feature releasing method of AE signals in grinding process also be discussed. The grinding state feature information can be updated automatically, and be browsed by remote experts through running the ActiveX in the browser. Therefore, it is of important significance for improving workers’ working environment and work-piece quality.