Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

2021 ◽  
Author(s):  
Chun-Wei Liu ◽  
Shiau-Cheng Shiu ◽  
Kai-Hung Yu
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Optical Glass ◽  
Removal Rate ◽  
Support Vector ◽  
Grinding Wheel ◽  
Emission Signal ◽  
Sensing Technology ◽  
Acoustic Emission Sensing ◽  
Circularity Error

Abstract A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of <E0.1, and an optical axis error of <150 μrad.

AE Signal of early Rotor Cracks Fault Diagnosis Based on PWVD and SVM

2011 ◽  
Vol 216 ◽  
pp. 212-217
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Kuan Fang He ◽  
X.C. Li
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Frequency Characteristic ◽  
Feature Vector ◽  
Support Vector ◽  
Early Prediction ◽  
Emission Signal ◽  
Reliable Diagnosis ◽  
Ae Signal

Aiming at inaccurately and inefficiently fault feature of early crack by the vibration method in the environment of strong noise, the acoustic emission signal (AE) is used to cracks defect with the advantages of sensitive. The Pseudo Wigner-Ville Distribution (PWVD) is introduced to extract the amplitude and frequency of AE signal as feature vector, which combines with support vector machine (SVM) to achieve prediction and diagnosis of fault types of different rotor cracks depth. It is shown by experiment that the proposed method have the features of obvious frequency characteristic, early prediction of fault time, accurate and reliable diagnosis results of early cracks fault diagnosis.

Parametric Study of Micro Machining Three Dimensional Microstructure with the Tiny-Grinding Wheel Based on the EMR Effect

2010 ◽  
Vol 447-448 ◽  
pp. 193-197
Author(s):  
Wei Qiang Gao ◽  
Qiu Sheng Yan ◽  
Yi Liu ◽  
Jia Bin Lu ◽  
Ling Ye Kong
Keyword(s):  
Magnetic Field ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Rotation Speed ◽  
Optical Glass ◽  
Removal Rate ◽  
Grinding Wheel ◽  
Machining Time ◽  
Fluctuation Phenomenon

Electro-magneto-rheological (EMR) fluids, which exhibit Newtonian behavior in the absence of a magnetic field, are abruptly transformed within milliseconds into a Bingham plastic under an applied magnetic field, called the EMR effect. Based on this effect, the particle-dispersed EMR fluid is used as a special instantaneous bond to cohere abrasive particles and magnetic particles together so as to form a dynamical, flexible tiny-grinding wheel to machine micro-groove on the surface of optical glass. Experiments were conducted to reveal the effects of process parameters, such as the feed rate of the horizontal worktable, feeding of the Z axis, machining time and machining gap, on material removal rate of glass. The results indicate that the feed rate of the worktable at horizontal direction has less effect on material removal rate, which shows a fluctuation phenomenon within a certain range. The feed rate of the Z axis directly influences the machining gap and leads to a remarkable change on material removal rate. Larger material removal rate can be obtained when the feeding frequency of Z direction is one time per processing. With the increase of rotation speed of the tool, material removal rate increases firstly and decreases afterwards, and it gets the maximum value with the rotation speed of 4800 rev/min. The machining time is directly proportional to material removal amount, but inversely proportional to material removal rate. Furthermore, material removal rate decreases with the increase of the machining gap between the tool and the workpiece. On the basis of above, the machining mode with the tiny-grinding wheel based on the EMR effect is presented.

scholarly journals Intelligent Fault Diagnosis of Synchromesh Gearbox Using Fusion of Vibration and Acoustic Emission Signals for Performance Enhancement

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
T Praveenkumar ◽  
M Saimurugan ◽  
K I Ramachandran
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Support Vector ◽  
Feature Level Fusion ◽  
Sensor Signals ◽  
Early Fault Detection ◽  
Ae Signals ◽  
Level Fusion

Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.

Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization

2015 ◽  
Vol 27 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Guimei Gu ◽  
◽  
Rang Hu ◽  
Yuanyuan Li
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Particle Swarm Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Damage Identification ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Upper Computer

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/03.jpg"" width=""340"" />Classification results of SVM-PSO</div> In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.

Identification Method of Tool Wear Based on Locally Linear Embedding and Support Vector Machine

2012 ◽  
Vol 246-247 ◽  
pp. 1289-1293
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Support Vector ◽  
Emission Signal ◽  
Identification Method ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Data Points ◽  
Linear Embedding

Aiming at the nonlinear characteristics of the tool wear Acoustic Emission signal, tool wear state identification method is proposed based on local linear embedding and vector machine supported. The local linear embedding algorithm makes high dimensional information down to low dimension feature space through commutation, and thus to compress the data for highlighting signal features. This algorithm well compensates for the weakness of linear dimension reduction failing to find datasets nonlinear structure. In this paper, acoustic emission signal is firstly made by phase space reconstruction. Using local linear embedding method, the high dimension space mapping data points are reflected into low-dimensional space corresponding data points, then extracting tool wear state characteristics, and using vector machine supported classifier to identify classification of the tool wear conditions. Experimental results show that this method is used for the exact recognition of the tool wear state, and has widespread tendency.

Material removal monitoring in precision cylindrical plunge grinding using acoustic emission signal

2013 ◽  
Vol 228 (4) ◽  
pp. 715-722 ◽  
Author(s):  
Chen Jiang ◽  
Haolin Li ◽  
Yunfei Mai ◽  
Debao Guo
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Material Removal ◽  
Grinding Force ◽  
Force Model ◽  
Precision Grinding ◽  
Emission Signal ◽  
Proposed Model ◽  
Acoustic Emission Sensing ◽  
Series Of Experiments

A mathematical model of the acoustic emission signal during a grinding cycle is proposed for the monitoring of material removal in precision cylindrical grinding. Acoustic emission signals generated during precision grinding are sensitive to forces in grinding and present opportunities in accurate and reliable process monitoring. The proposed model is developed on the basis of a traditional grinding force model. Using the developed model, a series of experiments were performed to demonstrate the effectiveness of the acoustic emission-sensing approach in estimating the time constant and material removal in grinding. Results indicate that acoustic emission measurements can be used in the prediction of material removal in precision grinding with excellent sensitivity.

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