Analysis Of Acoustic Emission Signals Research Articles

Acoustic emission signal analysis for crack growth resistance of ARALL. (In Japanese): Journal of the Japanese Society for Non-Destructive Inspection, Vol. 38, No. 2, pp. 104–112 (Feb. 1989)

Acoustic emission signal analysis for crack growth resistance of ARALL. (In Japanese): Journal of the Japanese Society for Non-Destructive Inspection, Vol. 38, No. 2, pp. 104–112 (Feb. 1989)

ファインセラミックスの引っかき溝形成の延性／ぜい性遷移のＡＥ法による識別

This paper deals with an acoustic emission signal analysis for identifying the transition from ductile to brittle groove formation in scratching fine ceramics. A series of scratching tests of representative fine ceramics with a conical diamond indenter have been conducted to investigate the characteristics of the acoustic emission signals generated in different scratching modes. Main results obtained in this paper are as follows : (1) The scratching process may be classified into following three types ; 1) ductile scratching mode in which no cracking is observed, 2) brittle scratching mode I in which the brittle cracks occur and 3) brittle scratching mode II in which extensive cracking and chipping along the grooves occur. (2) When the scratching process changes from ductile to brittle mode with an increase of scratching load, a sudden increasing in AE event count rate takes places. Therefore, the identifying of ductile/ brittle transition in scratching may be performed by using this phenomenon. (3) The AE signals generated during ductile scratching have the frequency components lower than 0.35MHz, while the AE signals during brittle scratching have high frequency components between 0.4-0.9MHz as well as the frequency components lower than 0.35MHz. Consequently, the ductile/brittle transition in scratching may be identified by recognizing the pattern of the power spectra of AE signals.

Acoustic emission signal analysis for crack growth resistance of ARALL. (In Japanese): Journal of the Japanese Society non-destructive inspection, vol. 38, no. 2, pp. 104–112 (Feb. 1989)

Acoustic emission signal analysis for crack growth resistance of ARALL. (In Japanese): Journal of the Japanese Society non-destructive inspection, vol. 38, no. 2, pp. 104–112 (Feb. 1989)

Adhesive and abrasive wear studies using acoustic emission techniques

This paper is concerned with the measurement and subsequent analysis of acoustic emission signals from lubricated sliding contacts. The results show that the time-dependent nature of the acoustic signal is able to detect the presence of wear-reducing additives and the predominant wear process occurring. Further, third-body abrasive wear tests show that both wear and acoustic emission signals increase with alumina particle concentration added to the lubricant. A direct empirical relationship between integrated r.m.s. signal and wear volume removed from the test ball in a ball-on-cylinder test apparatus has been obtained.

Identification of Chip Form in Turning Process.

The machining process can be considered as a planned interaction of workpiece, tool, and machine tool. In an unmanned situation, the results of this interaction are to be continuously monitored so that any changes in the machining environment can be sensed in order to take corrective action. In order to design the process monitoring system for unmanned manufacturing, the identification of chip form is proposed. The system proposes the method of using acoustic emission signal analysis to identify the chip form during cutting. The acoustic emission signals are analyzed using the standard deviation, the skew, and the kurtosis of the beta distribution and the counted frequency at the acoustic emission mode. The fuzzy set theory and the neural network are applied to identify the chip form. In fact, the percentage of correct recognition of the identification of thip form using both the methods is always higher than 90% in experiments.

Experimental studies of sliding friction and wear via acoustic emission signal analysis

Friction- and wear-related problems have been at the root of many of the limitations to productivity improvements in the manufacturing processes. Research studying acoustic emission (AE) generated by metal cutting and forming processes has shown that the potential of using AE in the investigation of friction and wear phenomena is very promising. The AE generated from the sliding contact of metal-metal pairs using pin-on-disk tests were investigated in this study. The usefulness of AE as an experimental tool for tribological studies related to friction and wear phenomena is evaluated. It is shown that AE is quite sensitive to the change in friction and wear mechanisms active in the sliding processes. The influence of process variables such as loading condition, sliding speed and distances on the AE generated from sliding contact is also studied.

Characterization of particle impact by quantitative acoustic emission

Acoustic emission (AE) is proposed as a method of monitoring hard particle impact on surfaces. In a preliminary study, small spherical particles of nominal diameter 53–63 μm and 75–90 μm of bronze and glass respectively were dropped in vacuum onto steel or aluminium target plates at selected velocities between 2.5 and 7.1 m s −1. At these low velocities the collisions are entirely elastic and no erosion occurs. However, a quantitative AE signal analysis method was developed which enabled various parameters from the particle impacts to be determined, with the potential for measuring the degree of plasticity of impact in later experiments. AE signals were detected with a calibrated broad bandwidth piezo-electric transducer positioned on the opposite face of the target plate at the impact epicentre. The Green's function for the plate, together with the AE transducer and system response obtained by calibration using a capacitance transducer, were deconvolved from AE signals to yield the impact force function of the particle impact. The velocity dependence of the impact time and peak impact force thus determined agreed with that predicted from semi-empirical models of elastic particle impact. The absolute magnitude of these two parameters also compared very well with theoretical values. The quantitative acoustic technique developed was capable of sizing the particles, and the results compared favourably with the particle size distribution measured optically, giving the correct mean particle diameter to within 10% and the distribution spread to within 26%. This fundamental approach provides a framework for later extension to plastic and partially plastic particle impact Thus the technique could be developed for on-line erosion monitoring.

Characterization of Sheet Metal Forming Using Acoustic Emission

The application of acoustic emission signal analysis for characterization of sheet metal forming operations is discussed in this paper. Two particular sheet metal forming operations are examined: punch stretching and deep drawing. The acoustic emission signal characteristics, including the energy content, the spectral properties, and time series behaviors, as functions of the process state, are experimentally studied. Using plastic work analysis, an analytical relationship between acoustic emission energy rate and punch stretching parameters (punch feed rate, workpiece thickness, punch size, holder diameter, amount of plastic deformation, and workpiece material properties) is developed and supporting experimental results presented. During the forming processes, acoustic emission signal features show strong correlations with punch/workpiece contact, yielding, deformation, flange wrinkling, necking and fracture. Therefore, acoustic emission can be effectively used for in-process monitoring of sheet metal forming operations.

Acoustic Emission and Signal Analysis

Acoustic emission (AE) is one of the most recent entries into the field of nondestructive evaluation. Due to the uniqueness of the basic principle and the potential for tackling a wide range of applications, the technique has gone through rapid strides in a very short time. Thus, today, two decades after the first application of the technique, AE is used in various industries, such as petro-chemical, refinery, nuclear, transportation and aerospace. While, some of the applications can be dealt with by current state-of-the-art, through simple methods of measurement and analysis, the entire potential of the technique still remains to be exploited as extraction of complete information contained in the signal is not possible with the adaption of only simple data analysis procedures. Currently several scientists in many countries are involved in evolving and implementing advanced concepts for AE signal analysis. These along with the approach adopted by us are discussed in this paper.

Statistical process control of acoustic emission for cutting tool monitoring

The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis. In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes. The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.

Frequency spectrum analysis of acoustic emission signal obtained during tensile deformation and fracture of an AISI 316 type stainless steel

Acoustic emission (AE) signals obtained during tensile deformation and fracture of an AISI 316 stainless steel specimens have been analysed in frequency domain. An attempt has been made to correlate the frequency spectra corresponding to various stages of plastic deformation with the physical phenomena occurring. By this mode of analysis, it was possible to distinguish two regions of plastic deformation namely the uniform elongation region upto ultimate tensile strength and necking deformation by their charecteristic patterns. The pattern in the uniform elongation region has indicated that during continuous type AE emission, the extent of waiting time of mobile dislocations at short range obstacles is mainly responsible for the predominant frequencies observed. The lower value of predominant frequency observed during the necking region is attributed to microvoid coalescence and/or crack growth, a less transient phenomenon.

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

Application of acoustic emission sensing to slip detection in robot grippers

This paper discusses the application of acoustic emission signal analysis as a sensing tool for the detection of contact and slip related motion between a workpiece and an end effector. Some results from past work in this area are first reviewed. Different modes of slip motion (rotational and translational) are then discussed. The ability to distinguish between these slip modes is important since it enables the robot to assess the gripping stability and recover positional control of the workpiece. Experimental results are presented to demonstrate the sensitivity of the detected acoustic emission signal to these different modes of slip motion. The effects of the gripping force on the detected acoustic emission is also discussed.

Linear discriminant function analysis of acoustic emission signals for cutting tool monitoring

A principal setback to automation of the machining process is the inability to completely monitor the condition of the cutting tool in real time. Whereas several of the techniques developed to date are useful in specific applications, no universally applicable sensor is yet available. Acoustic emission is one of the most promising techniques to be recently developed for on-line cutting tool monitoring. However, signal analysis is still an area that requires further investigation to enhance the potential of acoustic emission. For this purpose, frequency-based pattern recognition concepts using linear discriminant functions have been used in analysing acoustic emission signals generated during machining to distinguish between different signal sources, specifically chip formation, tool fracture, and chip noise. Five features were used for classification in the frequency range of 100 kHz to 1 MHz, with each feature consisting of a 20 kHz bandwidth, and were selected using the class mean scatter criterion. The coefficients of the discriminant functions were obtained by training the system using signals generated by each of the sources of interest. An AISI 1018 steel was machined using a titanium carbide-coated cutting tool. Cutting speeds ranged from 200 to 800 ft/min (1 to 4 m/sec) with feed rats of 0·0005 to 0·0075 in/rev (0·0133 mm/rev to 0·191 mm/rev) and depth of cut 0·17 in (4·32 mm). The results show a successful classification rate of 90% for tool breakage, while those for chip formation and chip noise were 97 and 86% respectively.

Quantitative analysis of acoustic emission signals

Acoustic emission (AE) signals emanating from various deformation processes in materials are of a broadband nature. It is also well established that the ultrasonic attenuation caused by grain boundary scattering in polycrystalline materials is strongly frequency dependent. It is worth noting that distortions of AE signals due to grain boundary scattering (Rayleigh or stochastic type) have been inadequately treated in some previous quantitative AE studies. We propose a quantitative approach to estimating AE source parameters with proper care taken of the frequency-dependent media attenuation. Without getting involved in the complete measurement of media attenuation, the present approach is designed to find some characteristic quantities in both the frequency and time domains that are least sensitive to attenuation. The node frequency in the amplitude spectra and the peak time in real-time signals are found to be the most suitable for the purpose. They can be used to infer the source parameters such as the final size and the average velocity of source expansion.

Acoustic Emission Sensing of Tool Wear in Face Milling

Acoustic Emission (AE) signal analysis was applied to on-line sensing of tool wear in face milling. Cutting tests were conducted on a vertical milling machine. AE signals, feed and normal components of cutting force and flank wear were measured and compared. A signal processing scheme for intermittent cutting forces and AE signals, based on the concept of time domain averaging (TDA) is proposed. The results indicate that both AE and cutting forces have parameters that correlate closely with flank wear.

Frequency analysis of AE signal obtained during breakaway oxidation and internal cracking of 2 1/4Cr-1Mo steel at 900�C

The acoustic emission (AE) signal obtained during breakaway oxidation and internal cracking of 2 1/4Cr-1Mo steel at 900°C was analysed over its frequency domain. Three regions, namely pre-breakaway, post-breakaway, and internal cracking were distinguished and confirmed by thermogravimetric analysis and scanning electron microscopy observations. The frequency analysis of the AE signal obtained in the three different regions showed three different characteristic patterns. Frequency spectra based on predominant frequencies were correlated with the physical phenomena happening during the course of oxidation.

Evaluation of fracture toughness and the micro-fracture mechanism of Cu-Be-Co alloy by means of AE spectrum analysis.

Using the Acoustic Emission (AE) technique, the relationship among the AE spectrum, the change of the fracture toughness and micro-fracture mechanism due to the ageing effect have been investigated for commercial Cu-Be-Co alloy. The AE signals could be classified into 6 groups by spectrum analysis through all materials aged at 300°C and 420°C for given times. The classified AE signals were plotted along with the load-displacement diagram following the order of emitted time and energy. By AE signal analysis, the fracture toughness and micro-fracture mechanism and its change at the crack tip due to the ageing effect are discussed. At the early stage of deformation, the facet created by the decohesion of the intermetallic inclusion and the precipitations emitted AE, which had a certain frequency component of 200-600 kHz, where the dominent frequency components depend on the ageing process. Increasing the deformation, facet was connected suddenly by the dimple fracture which led to the pop-in crack propagation. In this step, the AE had an audible frequency and the highest energy level was emitted. A model of the micro-fracture process and AE source is proposed, paying particular attention to void nucleation and its coalescence at the crack tip.

ACOUSTIC EMISSION AND MACHINING - PROCESS ANALYSIS AND CONTROL

ABSTRACT A quantitative model considering signal attenuation and tool flank wear is proposed to describe the influence of machining process parameters on the level of acoustic emission (AE) generation. The AE sensitivities to width of cut, feed rate, and hardness of material in three-dimensional cutting are experimentally studied. The application of AE signal analysis to chip formation detection, providing information for process control in unattended machining, is described.

In-Process Tool Fracture Detection

This paper investigates the feasibility of using acoustic emission (AE) signal analysis for the detection of the breakage as well as the chipping of a cutting tool during machining. Experiments were conducted on a lathe using conventional carbide insert tools under realistic cutting conditions. The tangential and feed forces were also measured for comparisons. The sensitivities of the AE signal and cutting forces to insert chipping and fracture are illustrated. The relationship between acoustic emission energy and the combined effect of fracture surface area and cutting load is discussed.