Vibration Analysis of ID Slicing Process and Wafer Measurement

2006 ◽  
Vol 315-316 ◽  
pp. 641-645 ◽  
Author(s):  
Xin Wei ◽  
Rui Wei Huang ◽  
Shao Hui Lai ◽  
Z.H. Xie
Keyword(s):  
Surface Roughness ◽  
Power Spectrum ◽  
Vibration Analysis ◽  
Machining Accuracy ◽  
Radial Vibration ◽  
Total Thickness ◽  
Axial Vibration ◽  
Vibration Signals ◽  
Measurement And Analysis ◽  
Inner Diameter

ID (inner-diameter) slicing is widely used in cutting ingots currently. In this paper, the deflection (axial vibration) and vibration (radial vibration) signals in different slicing conditions of the silicon wafers were measured online and analyzed. The effects of the vibration signals on the machining accuracy and surface roughness of sliced wafers were investigated based on the measurement and analysis of the surface roughness, warpage and TTV (total thickness vibration) of the sliced wafers. The results show that the changes of surface roughness, warpage and TTV of the sliced wafers exhibit approximately consistence with the changes of the power spectrums of the acquired vibration signals in different working stage of the blade. The vibration and deflection signals can give evidence of the changes in the cutting forces and blade performance during slicing. The power spectrum of the signals is useful for monitoring the blade wear and tension condition and predicting the surface quality and machining accuracy of the sliced wafers.

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.

Vibration analysis and combustion parameter evaluation of CI engine based on Fourier Decomposition Method

2021 ◽  
pp. 146808742098819
Author(s):  
Wang Yang ◽  
Cheng Yong
Keyword(s):  
Vibration Analysis ◽  
Decomposition Method ◽  
Combustion Process ◽  
Pressure Rise ◽  
Maximum Pressure ◽  
Vibration Signals ◽  
Loop Control ◽  
Fourier Decomposition ◽  
Surface Vibration ◽  
Rise Rate

As a non-intrusive method for engine working condition detection, the engine surface vibration contains rich information about the combustion process and has great potential for the closed-loop control of engines. However, the measured engine surface vibration signals are usually induced by combustion as well as non-combustion excitations and are difficult to be utilized directly. To evaluate some combustion parameters from engine surface vibration, the tests were carried out on a single-cylinder diesel engine and a new method called Fourier Decomposition Method (FDM) was used to extract combustion induced vibration. Simulated and test results verified the ability of the FDM for engine vibration analysis. Based on the extracted vibration signals, the methods for identifying start of combustion, location of maximum pressure rise rate, and location of peak pressure were proposed. The cycle-by-cycle analysis of the results show that the parameters identified based on vibration and in-cylinder pressure have the similar trends, and it suggests that the proposed FDM-based methods can be used for extracting combustion induced vibrations and identifying the combustion parameters.

Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering

2016 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
A. H. M. Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Wavelet Transforms ◽  
Fourier Transforms ◽  
End Milling ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machined Surface ◽  
Vibration Signals

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

scholarly journals The influence of the profile-dividing grinding strategy on the surface accuracy and roughness of a gear teeth

Mechanik ◽  
2018 ◽  
Vol 91 (8-9) ◽  
pp. 737-740 ◽  
Author(s):  
Piotr Zyzak ◽  
Paweł Kobiela ◽  
Arnold Brożek ◽  
Marek Gabryś
Keyword(s):  
Surface Roughness ◽  
Grinding Wheel ◽  
Machining Accuracy ◽  
Cylindrical Gear ◽  
Gear Teeth ◽  
Surface Accuracy ◽  
Number Of Passes

In the paper are presented investigation results of an effects of adopted strategy of profile-dividing grinding of a cylindrical gear teeth, performed on the Rapid Höfler 900 grinder, on machining accuracy and surface roughness of the teeth. The strategies have taken into considerations changes in the following parameters determining obtained results of the grinding: number of passes, number of leads, shaping method of the grinding wheel.

scholarly journals Measurement and Analysis of Surface Roughness in WS2 Solid Lubricant Assisted Minimum Quantity Lubrication (MQL) Turning of Inconel 718

Procedia CIRP ◽  
2016 ◽  
Vol 40 ◽  
pp. 138-143 ◽  
Author(s):  
Uma Maheshwera Reddy Paturi ◽  
Yesu Ratnam Maddu ◽  
Ramalinga Reddy Maruri ◽  
Suresh Kumar Reddy Narala
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Solid Lubricant ◽  
Minimum Quantity Lubrication ◽  
Minimum Quantity ◽  
Measurement And Analysis

Dynamic Translation of Surface Coarseness Into Whisker Vibrations

2008 ◽  
Vol 100 (5) ◽  
pp. 2852-2865 ◽  
Author(s):  
Eran Lottem ◽  
Rony Azouz
Keyword(s):  
Surface Roughness ◽  
High Frequency ◽  
Radial Distance ◽  
Translation Process ◽  
Tactile Information ◽  
Large Variability ◽  
Vibration Signals ◽  
Texture Discrimination ◽  
Linear Discriminant ◽  
Do So

Rodents in their natural environment use their whiskers to distinguish between surfaces having subtly different textures and shapes. They do so by actively sweeping their whiskers across surfaces in a rhythmic motion. To determine how textures are transformed into vibration signals in whiskers and how these vibrations are expressed in neuronal discharges, we induced active whisking in anesthetized rats, monitored the movement of whiskers across surfaces, and concurrently recorded from trigeminal ganglion (TG) neurons. We show that tactile information is transmitted through high-frequency micromotions superimposed on whisking macro motions. Consistent with this, we find that in most TG neurons, spike activity, and high-frequency micromotions are closely correlated. To determine whether these vibration signals can support texture discrimination, we examined their dependence on surface roughness and found that both vibration signals carry information about surface coarseness. Despite a large variability in this translation process, different textures are translated into distinct vibrations profiles. These profiles depend on whiskers properties, on radial distance to the surface, and on whisking frequency. Using the characteristics of these signals, we employ linear discriminant analysis and found that all whiskers were able to discriminate between different textures. While deteriorating with radial distance, this classification did not depend on whisking frequency. Finally, increasing the number of whisks and integrating tactile information from multiple whiskers improved texture discrimination. These results indicate that surface roughness is translated into distinct whisker vibration signals that result in neuronal discharges. However, due to the dynamic nature of this translation process, we propose that texture discrimination may require the integration of signals from multiple spatial and temporal sensory channels to disambiguate surface roughness.

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