Experimental Study on Intelligent Monitoring of Diamond Grinding Wheel Wear

2008 ◽  
Vol 392-394 ◽  
pp. 714-718 ◽  
Author(s):  
Bo Zhao ◽  
Bao Yu Du ◽  
W.D. Liu
Keyword(s):  
Condition Monitoring ◽  
Time Domain ◽  
Grinding Wheel ◽  
Wheel Wear ◽  
Intelligent Monitoring ◽  
Diamond Grinding Wheel ◽  
Grinding Wheel Wear ◽  
Diamond Grinding ◽  
Characteristic Signal ◽  
Wear Condition

In order to research the relationship between grinding wheel wear and the signal of grinding strength and grinding vibration, the grinding strength signal and grinding vibration signal under different wear condition were carried on digital processing by time-domain, frequency-domain, and wavelet-pocket analysis, and characteristic signal reflecting grinding wheel wear condition was obtained. Grinding wheel wear was monitored by time-domain statistics average value of grinding strength and energy value of three layers wavelet-pocket decomposition frequency band. The method how to set design parameters of neural network is introduced, and their value in condition monitoring network is determined. Mapping model of grinding wheel wear and characteristic signal is established. Recognition effect is satisfied in the experiment of grinding wheel wear condition monitoring. It confirmed the model is reliable and effective. The result shows that the new intelligent monitoring method is effective on monitoring grinding wheel deactivation condition. One new method of diamond grinding wheel wear condition monitoring under precision and ultra-precision grinding is introduced.

Wear of Diamond Grinding Wheel during Low Speed Dressing by Alumina

2015 ◽  
Vol 658 ◽  
pp. 120-124
Author(s):  
Tachai Luangvaranunt ◽  
Natthawat Tangkaratanakul ◽  
Patchanok Sakultantimetha
Keyword(s):  
High Hardness ◽  
Large Mass ◽  
Diamond Wheel ◽  
Grinding Wheel ◽  
Work Piece ◽  
Wheel Wear ◽  
Diamond Grinding Wheel ◽  
Diamond Grinding ◽  
Dressing Tool ◽  
Pin On Disk

Diamond grinding wheel is used in high precision grinding process, when work piece has a very high hardness. For a specific grinding interval, the wheel must be properly dressed, in order to remove swarf, sharpen the worn diamond grits, open up new diamond protrusions, and recondition the bond material. Dressing of diamond grinding wheel by alumina dressing tool has been simulated in a pin-on-disk machine in the research. Sharpening of the wheel is indicated by the increase of its roughness value, and surface microstructure with protruding sharp diamond grits. It was found that increasing of sliding distant from 100 to 500 m will increase the roughness of the wheel. The increase of contact load from 10 to 20 N will also increase roughness of the wheel, and the severity of wheel wear, indicated by high values of friction coefficient. A proper dressing of this nickel bonded SD1200 diamond wheel is by sliding against alumina dressing tool for at least 300 m under 10 N load. Sliding velocity has minimal effect to the results. A too large sliding distant and load will cause severe damage to wheel surface, and severe wheel wear, indicated by its large mass loss.

Schleifscheibenverschleiß bei der PKD-Bearbeitung*/Grinding Wheel Wear in PCD-Grinding

2018 ◽  
Vol 108 (06) ◽  
pp. 448-453
Author(s):  
F. Vits ◽  
D. Trauth ◽  
P. Mattfeld ◽  
F. Klocke
Keyword(s):  
Wear Mechanisms ◽  
Polycrystalline Diamond ◽  
Grinding Wheel ◽  
Test Rig ◽  
Wheel Wear ◽  
Systematic Analysis ◽  
Microscopic Scale ◽  
Diamond Grinding Wheel ◽  
Grinding Wheel Wear ◽  
Input Variables

Der Artikel beschreibt eine systematische Untersuchung des Verschleißes einer keramisch gebundenen Diamantschleifscheibe beim Schleifen von polykristallinem Diamant vom Typ CMX 850 bei variablen Prozesseingangsgrößen. Ein neu entwickelter Versuchsaufbau ermöglicht die Betrachtung eines fortschreitenden Schleifscheibenverschleißes auf mikroskopischer Skala und eine Erklärung der zugrundeliegenden Schleifscheibenverschleißmechanismen.   This Paper contains a systematic analysis of the wear of a vitrified bonded diamond grinding wheel in grinding of polycrystalline diamond CMX 850 with different process input variables. A newly developed test rig enables the observation of a continuous grinding wheel wear on a microscopic scale and an explanation of the underlying grinding wheel wear mechanisms.

An Intelligent Grinding Wheel Wear Monitoring System Based on Acoustic Emission

2017 ◽  
Vol 261 ◽  
pp. 195-200 ◽  
Author(s):  
Ning Ding ◽  
Chang Long Zhao ◽  
Xi Chun Luo ◽  
Jian Shi
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Monitoring System ◽  
Grinding Wheel ◽  
Wheel Wear ◽  
Grinding Wheel Wear ◽  
Wear Monitoring ◽  
Grinding Machine ◽  
Ae Signals ◽  
Wear Condition

Acoustic emission (AE) signals can provide tool condition that is critical to effective process control. However, how to process the data and extract useful information are challenging tasks. This paper presented an intelligent grinding wheel wear monitoring system which was embedded in a surface grinding machine. An AE sensor was used to collect the grinding signals. The grinding wheel wear condition features were extracted by a proposed novel method based on statistics analysis of the average wavelet decomposition coefficient. The detailed signal characteristics during different wear condition are described. A BP neural network was used to classify the conditions of the grinding wheel wear. The inputs of the neural network were the three extracted features, and the outputs were three different states of grinding wheel condition, namely primary wear, intermediate wear and serious wear. The intelligent monitoring system was evaluated through grinding experiments. The results indicate that the effectiveness of the proposed method for extracting features of AE signals and developed intelligent grinding wheel wear monitoring system are satisfied.

scholarly journals Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1054
Author(s):  
Guo Bi ◽  
Shan Liu ◽  
Shibo Su ◽  
Zhongxue Wang
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Diamond Wheel ◽  
Feature Representation ◽  
Grinding Wheel ◽  
Time Waveform ◽  
Original Time ◽  
Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

Characteristics of the Wheel Surface Topography in Ultra-precision Grinding of Silicon Wafers

2008 ◽  
Vol 389-390 ◽  
pp. 36-41
Author(s):  
Feng Wei Huo ◽  
Dong Ming Guo ◽  
Ren Ke Kang ◽  
Zhu Ji Jin
Keyword(s):  
Surface Topography ◽  
Sampling Interval ◽  
Grinding Wheel ◽  
Height Distribution ◽  
Wheel Surface ◽  
Diamond Grinding Wheel ◽  
Diamond Grinding ◽  
Cutting Surface ◽  
Ultra Precision ◽  
Cutting Direction

A 3D profiler based on scanning white light interferometry with a lateral sampling interval of 0.11μm was introduced to measure the surface topography of a #3000 diamond grinding wheel, and a large sampling area could be achieved by its stitching capability without compromising its lateral or vertical resolution. The protrusion height distribution of diamond grains and the static effective grain density of the grinding wheel were derived, and the wheel chatter and the deformation of the wheel were analyzed as well. The study shows that the grain protrusion height obeys an approximate normal distribution, the static effective grain density is much lower than the theoretical density, and only a small number of diamond grains are effective in the grinding process with fine diamond grinding wheel. There exists waviness on the grinding wheel surface parallel with the wheel cutting direction. The cutting surface of the grinding wheel is not flat but umbilicate, which indicates that the elastic deformation at the wheel edges is much larger than in the center region.

scholarly journals A study on dressing of resinoid bonded diamond grinding wheel.

1989 ◽  
Vol 55 (512) ◽  
pp. 1106-1109
Author(s):  
Yoongyo JUNG ◽  
Ichiro INASAKI ◽  
Satoshi MATSUl
Keyword(s):  
Grinding Wheel ◽  
Diamond Grinding Wheel ◽  
Diamond Grinding
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