Modeling and Analysis of Acoustic Emission in Diamond Turning

1996 ◽  
Vol 118 (2) ◽  
pp. 199-207 ◽  
Author(s):  
J. J. Liu ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Energy Content ◽  
Rolling Process ◽  
Quantitative Model ◽  
Diamond Turning ◽  
Modeling And Analysis ◽  
Process Characterization ◽  
Theoretical Predictions ◽  
Frequency Components ◽  
Ae Signal

To estimate the acoustic emission (AE) energy released in diamond turning, a quantitative model, which contains the energy from primary, secondary, tertiary cutting zones and the rubbing zones, is proposed and compared with experimental data. The purpose of this model is to assist in process characterization and monitoring. As part of the model developed here the plowing energy, that is, the energy released in the tertiary zone, is approximated by the forming load in the rolling process where the roller is stationary. This load is theoretically calculated by the upper bound method and used in the estimation. A series of diamond turning tests were conducted to check the validity of the model. It was found that the energy content of the AE signal is close to the theoretical predictions. The spectral analysis of the AE signal in these tests is also carried out. It was noticed that when the diamond tool first touches the workpiece without producing any chip, more high frequency components were observed and this stage was recognized as the rubbing stage. The results further support the previous findings, that is, that abnormal rubbing always increases the mean frequency of the raw AE signal.

Acoustic Emission Condition Monitoring of the Coroning Process

2009 ◽  
Author(s):  
Juil Yum ◽  
Amir Kamouneh ◽  
Wencai Wang ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Visual Observation ◽  
Tool Condition Monitoring ◽  
Selection Methods ◽  
Tool Failure ◽  
Minimum Error ◽  
Tool Condition ◽  
Frequency Components ◽  
Ae Signal

Acoustic emission (AE) is introduced for tool condition monitoring during the coroning process. The frequency components of the AE signal were used as features for classification. Two different feature selection methods were investigated, namely visual observation and the class mean scatter criterion. The minimum error rate Bayesian rule was used to distinguish between two extreme tool conditions. Although the features from visual observation could result in 100% classification, features based on the class mean scatter criterion showed excellent monitoring capability of tool failure when fewer features were used.

Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting

1981 ◽  
Vol 103 (3) ◽  
pp. 330-340 ◽  
Author(s):  
Elijah Kannatey-Asibu ◽  
David A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Metal Cutting ◽  
Energy Content ◽  
Energy Levels ◽  
Cutting Parameters ◽  
Signal Energy ◽  
Emission Signal ◽  
Orthogonal Metal Cutting ◽  
Ae Signal ◽  
Work Of Deformation

Theoretical relationships have been drawn between acoustic emission (AE) and the metal cutting process parameters by relating the energy content of the AE signal to the plastic work of deformation which generates the emission signals. The RMS value of the emission signal is expressed in terms of the basic cutting parameters. Results are presented for 6061-T6 aluminum and SAE 1018 steel over the range of speeds 25.2 to 372 sfm (0.128 to 1.9 m/s) and rake angles 10 to 40 deg. Good correlation has been found between predicted and experimental signal energy levels. In addition, AE generation from chip contact along the tool face is studied and the AE energy level reflects the existence of chip sticking and sliding on the tool face, and indicates the feasibility of utilizing AE in tool wear sensing.

A Study on Acoustic Emission from the Orthogonal Cutting Process

1990 ◽  
Vol 112 (3) ◽  
pp. 203-211 ◽  
Author(s):  
T. Blum ◽  
I. Inasaki
Keyword(s):  
Acoustic Emission ◽  
Energy Content ◽  
Orthogonal Cutting ◽  
Cutting Process ◽  
Machining Force ◽  
Ae Signal ◽  
Sensor Signals ◽  
Cutting Zone ◽  
Work Of Deformation ◽  
Ae Signals

Comprehensive experiments have been conducted to determine the influence of cutting conditions on the generation of Acoustic Emission (AE) signals while machining S45C steel. The simultaneous observation of AE-sensor signals and tool dynamometer signals provides extensive data on the orthogonal cutting process. Theoretical relationships drawn between the energy content of the AE-signal and the plastic work of deformation in the primary and secondary cutting zone will be discussed with these data. In addition, shortcomings of the established theory will be highlighted. A relationship between the AE-signals generated and the strain rate will be estimated. The influence of flank wear on the generation of AE signals will be emphasized. Finally, the feasibility of utilizing AE in tool wear sensing will be pointed out while comparing AE-signal generation and machining force measurements for the orthogonal cutting process.

Analysis of Acoustic Emission Signal Generation During Martensitic Transformation

1986 ◽  
Vol 108 (4) ◽  
pp. 328-331 ◽  
Author(s):  
Elijah Kannatey-Asibu ◽  
Dong Pingsha
Keyword(s):  
Acoustic Emission ◽  
Energy Content ◽  
High Strength Steels ◽  
High Strength ◽  
Signal Generation ◽  
Martensite Formation ◽  
Emission Signal ◽  
Chemical Free Energy ◽  
Ae Signal ◽  
Work Done

The formation of cold cracks during welding of high strength steels is almost always preceded by martensite formation. Real time detection of both the martensite and cracks formed is a basic necessity of automated welding systems. Acoustic emission has been found to be highly suited for this purpose. Unfortunately, most of the work done to date on AE generation during martensitic phase transformation has been qualitative in nature. This paper presents a quantitative analysis of AE signal generation during martensite formation, using an energy method. This formulation relates the chemical free energy change which is the driving force for the transformation to the RMS value which is a measure of the energy content of AE signals. The analysis shows that the RMS signal is dependent on carbon concentration, volume transformed, cooling rate, and temperature. This is consistent with previous experimental work.

A Model of Tool Fracture Generated Acoustic Emission During Machining

1987 ◽  
Vol 109 (3) ◽  
pp. 227-233 ◽  
Author(s):  
E. N. Diei ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Cutting Tools ◽  
Tool Material ◽  
High Amplitude ◽  
Quantitative Model ◽  
Stress Wave Propagation ◽  
Linear Elastic ◽  
Milling Operations ◽  
Ae Signal ◽  
Elastic Fracture

This paper presents details of a study of the fundamental nature of the high amplitude acoustic emission (AE) signal generated during the complete breakage of cutting tools. A quantitative model of the peak AE rms voltage, incorporating the effects of tool material and the area of the fracture surface, is proposed. The model, derived from results of Linear Elastic Fracture Mechanics, and stress wave propagation in solids, is shown to be in good agreement with results of notched carbide insert fracture during turning and face milling operations.

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

scholarly journals The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Acoustic Emission Analysis of Nanoindentation-Induced Fracture Events

2004 ◽  
Vol 841 ◽  
Author(s):  
Pawel Dyjak ◽  
Raman P. Singh
Keyword(s):  
Acoustic Emission ◽  
Brittle Materials ◽  
Signal Amplitude ◽  
Crack Arrest ◽  
Quantitative Information ◽  
Local Failure ◽  
Emission Analysis ◽  
Specimen Holder ◽  
Cube Corner ◽  
Ae Signal

ABSTRACTMonitoring of acoustic emission (AE) activity was employed to characterize the initiation and progression of local failure processes during nanoindentation-induced fracture. Specimens of various brittle materials were loaded with a cube-corner indenter and AE activity was monitored during the entire loading and unloading event using an AE transducer mounted inside the specimen holder. As observed from the nanoindentation and AE response, there were fundamental differences in the fracture behavior of the various materials. Post-failure observations were used to identify particular features in the AE signal (amplitude, frequency, rise-time) that correspond to specific types of fracture events. Furthermore, analysis of the parametric and transient AE data was used to establish the crack-initiation threshold, crack-arrest threshold, and energy dissipation during failure. It was demonstrated that the monitoring of AE signals yields both qualitative and quantitative information regarding highly local failure events in brittle materials.

Study on Acoustic Emission of 1-3 Orthotropic Cement Based Piezoelectric Sensors

2012 ◽  
Vol 487 ◽  
pp. 471-475 ◽  
Author(s):  
Shi Hui Xie ◽  
Mi Mi Li ◽  
Mei Juan Zhou ◽  
Min Sun ◽  
Shi Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Volume Fraction ◽  
Signal Strength ◽  
Signal Wave ◽  
Functional Material ◽  
Wave Shape ◽  
Sensing Element ◽  
Piezoelectric Composites ◽  
Ae Signal ◽  
Amplitude Decrease

1-3 orthotropic cement based piezoelectric composites were fabricated by cut-filling and arrange-filling technique, using PZT-51 ceramic as functional material and cement as passive matrix. 1-3 orthotropic cement based piezoelectric composites were prepared into Acoustic Emission (AE) sensors, the attenuation of AE signal on the concrete and the response of different sensors on the concrete with increasing distance were researched. The results showed that the signal strength received by sensing element increases with the increasing PZT volume fraction; signal peaks and amplitude decrease gradually when the testing distance increases; signal strength received on the ceramic title is stronger than on the concrete; the attenuation of signal wave shape received on the concrete is much slower when compared with ceramic title.

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