Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network

1998 ◽  
Vol 120 (4) ◽  
pp. 807-816 ◽  
Author(s):  
Y. M. Niu ◽  
Y. S. Wong ◽  
G. S. Hong ◽  
T. I. Liu
Keyword(s):  
Tool Wear ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Divide And Conquer ◽  
Time Frequency ◽  
Continuous Components ◽  
Wide Range ◽  
Ae Signal ◽  
Basis Wavelet ◽  
Tool Cutting

This paper proposes a new approach for multi-category identification of turning tool conditions. It uses the time-frequency feature information of the AE signal obtained from best-basis wavelet packet analysis. By applying the philosophy of divide-and-conquer and a local wavelet packet extraction technique, acoustic emission (AE) signals from turning process have been separated into transient and continuous components. The transient and continuous AE components are used respectively for transient tool conditions and tool wear identification. For transient tool condition identification, a 16-element feature vector derived from the frequency band value of wavelet packet coefficients in the time-frequency phase plane is used to identify tool fracture, chipping and chip breakage through an ART2 network. To identify tool wear status, spectral and statistical analysis techniques have been employed to extract three primary features: the frequency band power at 300 kHz–600 kHz, the skew and kurtosis. The mean and standard deviation within a moving window of the primary features are then computed to give three secondary features. The six features form the inputs to an ART2 neural network to identify fresh and worn state of the tool. Cutting experimental results have shown that this approach is highly successful in identifying both the transient and progressive tool wear states over a wide range of turning conditions.

scholarly journals Analysis of AE characteristics of tool wear in drilling CFRP/Ti stacked material

2018 ◽  
Vol 211 ◽  
pp. 03001
Author(s):  
Zhan Wang ◽  
Sheng Leng ◽  
Tao Min ◽  
Gang Chen
Keyword(s):  
Tool Wear ◽  
Wavelet Packet ◽  
Frequency Component ◽  
Fiber Reinforced Polymer ◽  
High Frequency Component ◽  
Drilling Process ◽  
Time Frequency ◽  
Reinforced Polymer ◽  
Characteristic Values ◽  
Stacked Material

Based on the signal data acquired in drilling process of carbon fiber reinforced polymer/titanium alloy (CFRP/Ti) stacked materials, the acoustic emission (AE) characteristic values were carefully studied, by using the method of statistical analysis, spectrum analysis and wavelet packet. The results show that the root mean square(RMS) value of the AE signals and the energy of the wavelet packet are closely related to the tool wear. Meanwhile, experiments indicate that different materials, chips and tool tipping will cause instantaneous signal mutation, which has different forms in time domain and in time-frequency domain. These mutations may increase the difficulty of identifying the tool wear. Fortunately, with repeated experiments and comparison, some identifiable mutations were recognized. When a tool is processed from CFRP to Ti, the signal intensity decreases generally, the high-frequency component of signal increases gradually, and the signal has a tendency to show in high frequencies.

The Study of Tool Wear and Breakage Based on the Characteristic Analysis of Acoustic Spectrum

2006 ◽  
Vol 532-533 ◽  
pp. 197-200 ◽  
Author(s):  
Quan Cheng Dong ◽  
Chang Sheng Ai ◽  
Na Wang
Keyword(s):  
Tool Wear ◽  
Frequency Band ◽  
Production Efficiency ◽  
Frequency Component ◽  
Machining Process ◽  
Characteristic Analysis ◽  
Tool Breakage ◽  
Time Frequency ◽  
Machining Center ◽  
Tool Monitoring

Tool monitoring is an important factor to restrict the improvement of production efficiency, machining quality and automation level. The monitoring of the tool wear and breakage conditions on YCM-V116B machining center was studied, and the acquired milling sound signals were analyzed in detail. By means of the classical time-frequency analysis, it was discovered that the wear sound had its own characteristic frequency band, and the frequency component within the frequency band would change according to the change of wear conditions. So that the frequency component within the frequency band will be a good indicator to monitor the tool wear conditions excellently. On the other hand, the tool breakage sound is a random signal that a transient change in amplitude is produced probably when tool breaks. The tool breakage conditions can be detected exactly by the advantages of wavelet decomposition techniques. The analysis implies that the sound generated during the machining process can be used to monitor tool conditions, which provides a new approach to the sound applications in tool monitoring domain.

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 564-568
Author(s):  
Chang Liu ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Lu Zhang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Quality ◽  
Machining Process ◽  
Work Piece ◽  
Time Frequency ◽  
Quality Of Work ◽  
On Line ◽  
Ae Signal

There is a high requirement on the surface quality of work-pieces made of Ni-based super-alloys due to the important application in aviation and aerospace fields, so it is particularly important to implement the on-line monitoring to the surface quality of work-piece in the machining process. The acoustic emission (AE) signal has the relatively superior signal/noise ratio and sensitivity during the process of nickel alloy. Through the analysis of AE signal’s characteristic which comes from the different condition of tool wear, it is an effective mean to evaluate the tool wear condition and monitor the surface quality of work-piece due to the usage of AE during the machining process. This paper indicate that it is simple and intuitive to achieve the on-line monitoring of surface quality which based on spectrum analysis of AE signal and proposed the method of on-line monitoring of the nickel alloy surface quality under different condition of tool wear based on AE time-frequency spectrum.

The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Harmonic Wavelet Packet and SVM

2011 ◽  
Vol 52-54 ◽  
pp. 2039-2044
Author(s):  
Fen Lou Zhai ◽  
Li Xin Gao ◽  
Neng Chun Gong ◽  
Yong Gang Xu ◽  
Ming Shi Feng
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Experimental Result ◽  
Support Vector ◽  
Fault Pattern ◽  
Fault Type ◽  
Harmonic Wavelet ◽  
Ae Signal

As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the harmonic wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the Harmonic wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines harmonic wavelet packet decomposition and SVM together can be effective.

A Method of Tools AE Signal Feature Extraction Based on Wavelet Packet Analysis

2011 ◽  
Vol 474-476 ◽  
pp. 189-194 ◽  
Author(s):  
Lan Shen Guo ◽  
Nai Qiang Dong ◽  
Wei Tian ◽  
Fang Zhong Zhang ◽  
Cai Xiao Li
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Tool Wear ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Signal Frequency ◽  
Frequency Range ◽  
Wavelet Packet Analysis ◽  
Ae Signal ◽  
The Will

The paper developed a method of tools AE signal feature extraction based on wavelet packet analysis, we used wavelet packet analysis to decompose acoustic emission signals for different frequency range of the signal components. The will occupy of the main energy of AE signal frequency range is extracted as the recognition feature vector of tool wear. The method has higher accuracy according to experiments.

The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Redundant Lifting Wavelet Packet and SVM

2011 ◽  
Vol 52-54 ◽  
pp. 2033-2038
Author(s):  
Li Xin Gao ◽  
Fen Lou Zhai ◽  
Bang Xi Hu ◽  
Jiang Hua Zhou ◽  
Jian Hua Chen ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Experimental Result ◽  
Support Vector ◽  
Fault Pattern ◽  
Fault Type ◽  
Ae Signal ◽  
Lifting Wavelet

As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the redundant lifting wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the redundant lifting wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines redundant lifting wavelet packet decomposition and SVM together can be effective.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals A Novel Ultrasonic TOF Ranging System Using AlN Based PMUTs

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 284
Author(s):  
Yihsiang Chiu ◽  
Chen Wang ◽  
Dan Gong ◽  
Nan Li ◽  
Shenglin Ma ◽  
...  
Keyword(s):  
Clock Cycle ◽  
High Accuracy ◽  
Ultrasonic Waves ◽  
Oxide Semiconductor ◽  
Average Error ◽  
Cmos Process ◽  
Clock Frequency ◽  
Time Frequency ◽  
Range Finding ◽  
Wide Range

This paper presents a high-accuracy complementary metal oxide semiconductor (CMOS) driven ultrasonic ranging system based on air coupled aluminum nitride (AlN) based piezoelectric micromachined ultrasonic transducers (PMUTs) using time of flight (TOF). The mode shape and the time-frequency characteristics of PMUTs are simulated and analyzed. Two pieces of PMUTs with a frequency of 97 kHz and 96 kHz are applied. One is used to transmit and the other is used to receive ultrasonic waves. The Time to Digital Converter circuit (TDC), correlating the clock frequency with sound velocity, is utilized for range finding via TOF calculated from the system clock cycle. An application specific integrated circuit (ASIC) chip is designed and fabricated on a 0.18 μm CMOS process to acquire data from the PMUT. Compared to state of the art, the developed ranging system features a wide range and high accuracy, which allows to measure the range of 50 cm with an average error of 0.63 mm. AlN based PMUT is a promising candidate for an integrated portable ranging system.

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