AE Signals Research Articles

Automatic pattern identification of rock moisture based on the Staff-RF model

Studies on the moisture and damage state of rocks generally focus on the qualitative description and mechanical information of rocks. This method is not applicable to the real-time safety monitoring of rock mass. In this study, a musical staff computing model is used to quantify the acoustic emission signals of rocks with different moisture patterns. Then, the random forest (RF) method is adopted to form the staff-RF model for the real-time pattern identification of rock moisture. The entire process requires only the computing information of the AE signal and does not require the mechanical conditions of rocks.

Procena zaostalog napona prilikom zavarivanja trenjem aluminijumskih ploča pomoću akustične emisije i ultrazvučnih talasa

The residual stress assessment in structures is essential for optimization of the structures' design. The attention of this paper is focused on how acoustic emission signals caused by tensile loading of the friction stir welded aluminum plates are expected to vary depending upon the residual stress. To this aim, the distribution of residual stresses in two friction stir welded aluminum specimens was firstly evaluated by ultrasonic stress measurement. AE signals were then produced during tensile tests and captured using AE sensors. The obtained AE signals were analyzed using statistical features including crest factor, cumulative crest factor and sentry function. It was found that the crest factor could be used to identify the presence of the residual stresses and that the trends of sentry function are in good agreement with the results of crest factor and cumulative crest factor.

Investigation on non-invasive process monitoring of Die Sinking EDM using Acoustic Emission signals

Die sinking electrical discharge machining is used extensively to produce very complex geometries on difficult-to-machine materials for aerospace, automotive, mold and die applications. The quality of the component produced mainly depends on the quality and intensity of spark generated between the tool and the workpiece. The present research work focused on using the acoustic emission signals to characterize the spark activity in the EDM process under different machining conditions. Experiments were conducted by varying the machining conditions and using three tool electrode materials namely graphite, copper and copper tungsten. The acoustic emission signal features were extracted from the data collected from different experimental conditions and are related to the various parameters including current, voltage, pulse on-time, surface roughness parameters (Ra and Rq) of the workpiece and material removal rate (MRR). The effect of tool material on the AE phenomenon was also investigated. The process conditions were found to have to have a good correlation with the acoustic emission signals features. Multiple linear regression models were built to predict the surface roughness (Ra) of the workpiece and MRR using only the current and pulse on-time as predictor variables. The prediction accuracy of the models improved by the addition of AE signal features to the predictor variable set. The results obtained indicate a suitability of employing acoustic emission signals for monitoring the spark activity, MRR and surface finish of the workpiece in the EDM process. Based on this, a reliable monitoring and diagnosis tool for EDM can be developed using the AE sensor and provides an opportunity to implement the industry 4.0 initiatives effectively.

Investigation on Parametric Process Optimization of HCHCR in CNC Turning Machine Using Taguchi Technique

In present scenario, production of any product efficiently with reduced machining time, high accuracy, low surface roughness and high productivity are the major issues. Further, machining parameters plays a vital role in manufacturing a product. Hence, it is necessary to determine the influence of Rate of Metal Removal (MRR) during machining and surface roughness (Ra) after machining. This paper examines the parametric optimization of High Carbon High Chromium Steel (HCHCR) material by using the results after experimenting in CNC turning machine. Its tool tip made of carbide. Inserts are used for producing the lower surface roughness while machining. The process variables considered for optimization such as spindle speed, rate of feed and depth of cut (DOC) in dry turning operation. TAGUCHI TECHNIQUE is used for optimization. Input variables are taken from the experiment. These are analyzed effectively to predict the Ra and MRR using signal to noise ratio, equation of Regression and Variance analysis. Further, an orthogonal array, L9 TAGUCHI TECHNIQUE is applied to identify the performance characteristic affecting surface roughness in turning process. Regression models are developed and validated to predict the surface roughness and AE Signal value. From the analysis, the optimized process parameters are obtained using TAGUCHI TECHNIQUE and L9 orthogonal array.

Application of machine learning to AE signal classification

Application of machine learning to AE signal classification

A Study on CVD Diamond Coated Cutting Tools Wear Performance using Vibration and Acoustic Emission Signals

Maximizing the use of cutting tool inserts and effectively utilizing them in the automated manufacturing process is critical to reducing machine down time and improving part quality. Early removal of tooling inserts can lead to less productivity and increased costs. Utilization of tooling inserts beyond its stipulated life time can cause dimensional inaccuracies and undesirable surface deterioration. Optimizing the cutting tool insert life will require an appropriate method for finding out remaining useful life of tooling inserts based on respective machining conditions. In this present work, acoustic signals and vibration signals were acquired during machining to monitor the tool failure. The change in signal variations in acoustic emission and vibration signals were correlated to the corresponding tool wear during machining. In this regard, machining experiments were performed on AA2124/25%/SiCp material using four different CVD Diamond coated tungsten carbide cutting tools (MCD-Microcrystalline diamond coating, NCD-Nano-crystalline diamond coating, BDD-Boron doped diamond coating, BMTN-Boron doped graded layer diamond coating) at an suitable cutting conditions. The results indicated that burst type AE signals observed in MCD and NCD coated tools correlated to chip entanglement, edge chipping, and the corresponding vibration analysis shows a similar increase in amplitude in the final cutting pass. However, there was no abnormality seen in BDD and BMTN coated tools signal variation which indicates least tool wear.

A comparative feature analysis for gear pitting level classification by using acoustic emission, vibration and current signals

this paper addresses the comparison of features, extracted in the time domain, from vibration, acoustic emission, and current signals, for the identification of eight levels of severity of pitting in a gearbox. The vibration, acoustic emission, and current signals were first acquired using a gearbox lab experimental test bed. Then, twenty features were extracted in the time domain from each signal; these features are ranked by Chi squared and entered into the KNN classifier, which allows the evaluation of the classification accuracy for each acquired signal and performing an analysis of the features. The results indicate that the vibration and AE signals identified the pitting level better than the current signal.

Investigation of Relation between Fracture Scale and Acoustic Emission Time‐Frequency Parameters in Rocks

To investigate relation between fracture scale and acoustic emission time‐frequency parameters in rocks, experiments of acoustic emission monitoring of granite uniaxial compression were carried out. The AE signal energy and dominant‐frequency of granite fracture process were extracted by means of AE time‐frequency analysis. The relation between fracture scale and AE time‐frequency parameters (energy and frequency) in granite fracture process was analyzed. The evaluation model of rock fracture scale based on AE energy and dominant‐frequency was established by using the intrinsic relation between the scale of rock fracture and the time‐frequency parameters of rock mass. The evolution of crack scale in the process of uniaxial compression was analyzed based on the evaluation model of rock fracture scale. Results show that the AE energy and the dominant‐frequency can reflect the crack scale inside the rock. The scale of rock fracture is proportional to the AE energy, which is inversely proportional to the AE dominant‐frequency. Signals with low frequency and high energy usually represent large‐scale cracks. On the contrary, if the high frequency has low energy value, it indicates a small‐scale crack. The theory and method of evaluation of rock rupture scale based on AE time‐frequency information (energy, frequency) can describe the failure process of rock crack scale variation characteristics. It provides a way and method for investigating the characterization of fracture size evolution process of rock fracture.

Damage Identification Method Based on Deconvolution Analysis of AE Signal in CFRP Laminates

Damage Identification Method Based on Deconvolution Analysis of AE Signal in CFRP Laminates

마이크로 밀링을 이용한 미세유로가공에서 미세버 저감방안에 관한 연구

Micro-burrs deteriorate machined surface quality and cause damage to the contact surfaces of parts; therefore, they should be removed through the deburring process. However, micro-burrs are not easy to remove, making it necessary to suppress the generation of micro-burrs by optimizing the machining process and advanced methodology. In this paper, an effective machining method to suppress microburrs and the relationship between burr size and cutting signals were investigated in the micro-milling process. In order to derive a machining method to minimize the generation of micro-burrs, a micro-channel was machined by a total of four machining methods, the size of the exit burr and top burr were measured, and an effective machining method was selected in based on the analysis of micro-burr generation. The relationship between the micro-burrs and the AE signals was determined by an analysis of cutting signals and micro-burr shape.

Identifying bubble occurrence during pool boiling employing acoustic emission technique

This paper reports the results of a study for the early detection of bubble formation during the boiling process using acoustic emission. The feasibility of using AE technology to detect and monitor early bubble formation during pool boiling is assessed, and the results show that AE technology is an affective tool for this purpose. There is a clear correlation between the AE signal levels and height of the water level above the heated surface during the boiling process. The different types of heated fluid influence AE energy levels during the bubble formation process. Statistically, it was found that the best AE parameters to indicate bubble formation were AE-RMS, AE-Energy and AE-Amplitude.

Investigation of the mechanical behavior of MAX phases by acoustic emission technique

Mechanical properties of two ternary carbides at room temperature were investigated under cyclic bending test with simultaneous acoustic emission monitoring (AE). Results show significant differences between both of them in terms of observed microstructural changes associated to acoustic emission response. Thanks to this technique coupled with an accurate micrographic investigation by SEM, mechanisms such as debonding of layers in MAX phases grains or damage occurrence in secondary intermetallic phases (microcracking) were identified and their specific acoustic signatures separated populations of AE signals were distinguished. In addition, bending test were also conducted at specific temperatures (1073K and 1473K) in order to investigate the brittle-to-ductile transition of both materials.

AE Test of Calcareous Sands with Particle Rushing

AbstractThe particle of calcareous sands was forced to crush, then the energy from the crushing was released by the form of sound waves. Therefore the AE technique was used to detect the calcareous sands AE signal when it crushed. by to study the AE characteristics, the mechanics of calcareous sands was studied. Study showed that: (1) there was the AE activities on the low confining pressure condition at the beginnig of test, (2) there was more and more AE activities with the continuing of test until to the end, (3) the calcareous sands’ AE activities was on the whole testing, (4) the calcareous sands’ particle crushing and mutual friction played different roles for its AE activities. Then the AE model based on the calcarous sands’ particle crushing was discussed.

Indication of the Destruction Stage of Epoxy PES-Filled Composite Using the Connected Parameter of Acoustic Emission Rate and the Spectral Characteristics of AE Signals

Abstract The methods of non-destructive testing have an important role in the problem of maintenance of transport and transport infrastructure. They make possible the early detection of internal defects in engineering structures and elements of vehicle design. The samples of composite materials used in the construction of transport infrastructure elements and transport vehicles have been tested. This paper shows the features of AE, which may allow more efficient and accurate analysis of the state of composite objects. The relation of the intensity and frequency characteristics of signals has been revealed. This work discusses the possibility of unambiguous identification of the stage of composite object destruction on the basis of these parameters.

Identification of electrical discharge on electric insulation material using acoustic emission method

Insulation system is essential to be equipped on high voltage equipment. One of the tests on insulation system is the electrical discharge test. In this work, the electrical discharge detected using an acoustic emission method to find out the characteristic of the discharge. Epoxy resin was taken as the insulation material, whereas one to two artificial holes were made inside the insulation. An AE sensor placed at a distance of 24 mm and 36 mm away from holes or discharge source. The main frequency component of the detected AE signal on single hole test was approximately 975 kHz. Meanwhile, test on double holes caused two main frequency component; i.e. 63 kHz and 930 Khz. For single hole test and sensor distance of 36 m, the AE signal intensity tends to be worth 40% from the AE signal at a distance of 24 mm.

TCM system in contour milling of very thick-very large steel plates based on vibration and AE signals

This paper describes the design and test of a tool condition monitoring (TCM) system during a milling process with an especial king-size multiple insert tool for machining very high thickness and length steel plates. The system focuses on the study of acoustic emission and vibration signals in order to correlate both physical phenomena with tool wear condition. By combining acoustic emission signals and vibration signals, it is possible to identify the tool condition with more reliability. Each of these signals provides complementary results, in different spectral bands. The use of different statistical measurements (RMS, kurtosis and skewness) together with the analysis by frequency bands allows identifying the tool condition and the transition between wear conditions (progressive, intermediate and advanced). The design of the TCM system for signals acquisition and processing is described (instruments and strategies). This work is innovative since there is no prior information about TCM systems applied to machining processes in such severe conditions. This type of control is essential in operations where very high value parts and tools are involved, such as milling of very thick-very large steel sheets.

Online evaluation of metal burn degrees based on acoustic emission and variational mode decomposition

Abstract Metal burn is encountered very often in industrial practice. Different degree of metal burn leads to unlike damage to related parts. In order to monitor the burn degrees accurately, AE burn signals are studied and the total process is given in the following steps. Firstly, four different degrees of surface burn are generated in experiment, during which AE signals are collected. Secondly, frequency shift regulation is found. It is explained by the phase transition principle and utilized to distinguish the burn degrees. Then, Variational Mode Decomposition (VMD) method is applied to extract main frequency of AE burn signals. And the input parameters of VMD are selected by scale-space method and comparison of anti-noise performance. Finally, the four degrees of metal burn are recognized by feature parameters successfully.

Acoustic emission characteristics of a single cylinder diesel generator at various loads and with a failing injector

Acoustic emission sensing techniques have been applied in recent years to dynamic machinery with varying degrees of success in diagnosing various component faults and distinguishing between operating conditions. This work explores basic properties of acoustic emission signals measured on a small single cylinder diesel engine in a laboratory setting. As reported in other works in the open literature, the measured acoustic emission on the engine is mostly continuous mode and individual burst events are generally not readily identifiable. Therefore, the AE are processed into the local (instantaneous) root mean square (rms) value of the signal which is averaged over many cycles to obtain a mean rms AE in the crank angle domain. Crank-resolved spectral representation of the AE is also given but rigorous investigation of the AE spectral qualities is left to future study. Cycle-to-cycle statistical dispersion of the AE signal is considered to highlight highly variable engine processes. Engine speed was held constant but load conditions are varied to investigate AE signal sensitivity to operating condition. Furthermore, during the course of testing the fuel injector developed a fault and acoustic emission signals were captured and several signal attributes were successful in distinguishing this altered condition. The sampling and use of instantaneous rms acoustic emission signal demonstrated promise for non-intrusive and economical change detection of engine injection, combustion and valve events.

Study of the transition from EHL to BL regions under friction of Ag and Ni. I. Analysis of acoustic emission

The main goal of this work is the study of the interaction between friction and the AE parameters for materials with different stacking fault energy (SFE) - Ni and Ag. The effects of load on the friction coefficient, wear rate, temperature and AE signals were studied. In order to evaluate a transition from EHL to BL regions, the Stribeck curves were analyzed. It was shown that the waveforms characterize the friction both in the macro scale (contact spot interaction) and microscale (single contact interaction). Low frequency range characterizes mainly shearing and damage in contact spots, while high frequency range indicates single interactions and plastic deformation at these points. The models of friction related to the waveforms in different lubricant conditions are considered.

Independent Component Analysis and Comparative Analysis of Oil and Acoustic Emission Technique for Condition Monitoring of Diesel Engines

Engine is the heart of an automobile. The good conditions of components ensure the better performance of engine and the condition of components can be monitored through as follows, engine oil analysis, vibration analysis, infra red thermograph and non destructive tests. This paper discusses the possibility of wear or failure in the engine components by analyzing the engine oil and acoustic emission signals. As the engine oil is like life blood for engine, we can get rich data about the healthy condition by analyzing it like doctors analyzing our health through urine. And Analysis has done for various properties like viscosity, volatile matters, total base number, total acid number, ash content, flash point and wear metals particles like Iron, aluminum, copper, chromium, lead, and zinc in order to monitor the wear of the engine components. The properties of the oil will be changed based on the number of cycles over time period. The used oil can be collected from the engine and analyzed. And acoustic emission it is entirely different from traditional condition monitoring methods. It can give the condition of inner parts through the AE signals. The properties of engine oil as well as the AE signals are getting changes as the number of cycle and time increases.