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.

Assessment of Tool Wear in Milling Using Acoustic Emission Detected by a Fiber-Optic Interferometer

1996 ◽  
Vol 118 (3) ◽  
pp. 428-433 ◽  
Author(s):  
T. A. Carolan ◽  
D. P. Hand ◽  
J. S. Barton ◽  
J. D. C. Jones ◽  
P. Wilkinson ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Fiber Optic ◽  
Cutting Tools ◽  
Piezoelectric Sensor ◽  
Absolute Calibration ◽  
Limited Bandwidth ◽  
The Face ◽  
Ae Signal ◽  
Fiber Optic Interferometer

Acoustic emission (AE) has previously been shown to be a useful technique in monitoring the state of wear of cutting tools. The piezo-electric transducers conventionally used for AE detection are contacting devices with a limited bandwidth. This paper describes the use of a robust fiber optic interferometer for the in-process measurement of AE during the face milling of annealed En24 steel to provide tool wear information via analysis of the rms AE signal. The interferometer displayed an improved diagnostic capability over a conventional piezoelectric sensor due to its advantages of being non-contacting, broadband with a flat frequency response to displacement, and providing absolute calibration.

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.

scholarly journals Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal

2019 ◽  
Vol 9 (20) ◽  
pp. 4368 ◽  
Author(s):  
Bach Phi Duong ◽  
Jaeyoung Kim ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Power Plants ◽  
Thermal Power ◽  
High Amplitude ◽  
Leakage Detection ◽  
Boiler Tube ◽  
Emission Signal ◽  
Coal Fuel ◽  
Ae Signal

Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the measured acoustic emission (AE) signal from leakages. It is essential to detect and practically handle these kinds of impulses. Based on the object detection concept, this paper proposes an impulse detection methodology that employs deep learning flexible boundary regression (DLFBR). First, the shape extraction (SE) preprocessing technique is implemented to yield the shape signal, which contains intrinsic information about the impulse from the raw AE signal. Then, DLFBR extracts and generates both the feature map and the confidence mask from the shape signal to regress a boundary box, which specifies the position of the impulse. For illustration purposes, the proposed algorithm is applied to an experimental leakage detection dataset recorded from a subcritical boiler unit with a tube membrane. Experimental results show that the proposed method is effective for detecting impulses of leakage in a boiler tube testbed, providing 99.8% average classification accuracy.

scholarly journals Quantitative Acoustic Emission Fatigue Crack Characterization in Structural Steel and Weld

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Adutwum Marfo ◽  
Ying Luo ◽  
Chen Zhong-an
Keyword(s):  
Acoustic Emission ◽  
Fatigue Crack ◽  
Structural Steel ◽  
Crack Growth ◽  
Wavelet Packet ◽  
Growth Characteristics ◽  
Three Point Bending ◽  
Linear Elastic ◽  
Elastic Fracture ◽  
Frequency Feature

The fatigue crack growth characteristics of structural steel and weld connections are analyzed using quantitative acoustic emission (AE) technique. This was experimentally investigated by three-point bending testing of specimens under low cycle constant amplitude loading using the wavelet packet analysis. The crack growth sequence, that is, initiation, crack propagation, and fracture, is extracted from their corresponding frequency feature bands, respectively. The results obtained proved to be superior to qualitative AE analysis and the traditional linear elastic fracture mechanics for fatigue crack characterization in structural steel and welds.

Fatigue Failure Analysis Using the Theory of Critical Distance

2011 ◽  
Vol 462-463 ◽  
pp. 663-667 ◽  
Author(s):  
Ruslizam Daud ◽  
Ahmad Kamal Ariffin ◽  
Shahrum Abdullah ◽  
Al Emran Ismail
Keyword(s):  
Stress Concentration ◽  
Fatigue Failure ◽  
Notch Root ◽  
High Stress ◽  
Critical Distance ◽  
Future Research ◽  
Element Analysis ◽  
Linear Elastic ◽  
The Finite Element Analysis ◽  
Elastic Fracture

This paper explores the initial potential of theory of critical distance (TCD) which offers essential fatigue failure prediction in engineering components. The intention is to find the most appropriate TCD approach for a case of multiple stress concentration features in future research. The TCD is based on critical distance from notch root and represents the extension of linear elastic fracture mechanics (LEFM) principles. The approach is allowing possibilities for fatigue limit prediction based on localized stress concentration, which are characterized by high stress gradients. Using the finite element analysis (FEA) results and some data from literature, TCD applications is illustrated by a case study on engineering components in different geometrical notch radius. Further applications of TCD to various kinds of engineering problems are discussed.

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.

Investigation on Stress and Strain Singularity in LEFM

2006 ◽  
Vol 306-308 ◽  
pp. 31-36
Author(s):  
Zheng Yang ◽  
Wanlin Guo ◽  
Quan Liang Liu
Keyword(s):  
High Stress ◽  
Crack Tip ◽  
Plane Stress State ◽  
Stress And Strain ◽  
Linear Elastic ◽  
Geometrical Nonlinear ◽  
Maximum Crack ◽  
Elastic Fracture ◽  
Plain Strain ◽  
Strain Singularity

Stress and strain singularity at crack-tip is the characteristic of Linear Elastic Fracture Mechanics (LEFM). However, the stress, strain and strain energy at crack-tip may be infinite promoting conflicts with linear elastic hypothesis. It is indicated that the geometrical nonlinear near the crack-tip should not be neglected for linear elastic materials. In fact, the crack-tip blunts under high stress and strain, and the singularity vanishes due to the deformation of crack surface when loading. The stress at crack-tip may still be very high even though the singularity vanishes. The low bound of maximum crack-tip stress is the modulus of elastic in plane stress state, while in plain strain state, it is greater than the modulus of elastic, and will increase with the Poisson’s ratio.

A New Specimen for Measuring the Interfacial Toughness of Al-0.5%Cu Thin Film on Si Substrate

2005 ◽  
Vol 297-300 ◽  
pp. 521-526
Author(s):  
Insu Jeon ◽  
Masaki Omiya ◽  
Hirotsugu Inoue ◽  
Kikuo Kishimoto ◽  
Tadashi Asahina
Keyword(s):  
Thin Film ◽  
The Finite Element Method ◽  
Si Substrate ◽  
Detailed Structure ◽  
Micro Fabrication ◽  
Interfacial Toughness ◽  
Linear Elastic ◽  
Elastic Fracture ◽  
Cu Thin Film ◽  
Research Show

A new specimen is proposed to measure the interfacial toughness between the Al-0.5%Cu thin film and the Si substrate. The plain and general micro-fabrication processes are sufficient to fabricate the specimen. With the help of the finite element method and the concepts of the linear elastic fracture mechanics, the detailed structure for this specimen is modeled and evaluated. The results obtained from this research show that the proposed specimen provides efficient and convenient method to measure the interfacial toughness between the Al-Cu thin film and the Si substrate.

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