Grey Prediction of CBN Grinding Process

2011 ◽  
Vol 5 (3) ◽  
pp. 420-426
Author(s):  
Neng-Hsin Chiu ◽  
◽  
Jie-Wei Lee
Keyword(s):  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Surface Grinding ◽  
Machining Process ◽  
Grey Model ◽  
Grinding Process ◽  
Grey Prediction ◽  
Ae Energy ◽  
Out Of Sample

Surface grinding is a machining process with unstable quality which is usually deteriorated as the process proceeds. If grinding can be forecast to alarm before unsatisfactory, the process could be controlled better. The purpose of this paper is to construct a grey model for CBN grinding based upon acoustic emission (AE) energy extracted from the AE grinding signal to reflect ground roughness variation. A grey model from the conducted experiment was found to be well correlated with the grinding trends. The prediction accuracy, inor out- of- sample, exceeds 90%, making grey prediction suitable for prognostic monitoring of grinding.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals Experimental Study on The Effect of Cross Feed of Surface Grinding on the Vibration and the Surface Roughness of Hardened Tool Steel OCR12VM

2020 ◽  
Vol 11 (3) ◽  
pp. 313-322
Author(s):  
Chairul Anam ◽  
◽  
Khairul Muzaka ◽  
Dian Ridlo Pamuji
Keyword(s):  
Surface Roughness ◽  
Tool Steel ◽  
Dimensional Accuracy ◽  
Surface Grinding ◽  
Machining Process ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Hardened Tool Steel ◽  
Stone Type ◽  
Grinding Stone

The grinding process is a machining process to obtain qualified surface roughness levels and high dimensional accuracy. There are two types of processes in the grinding process, namely the roughening and finishing processes. The vibration effect of the roughing process can damage and shorten the life of the tool/machine, while in the finishing process, the effect of vibration will reduce the dimensional accuracy, shape, and surface smoothness of the workpiece. This study aims to determine the effect of crossfeed on the amplitude of vibration and surface roughness of the workpiece on the surface grinding process. The materials used are hardened tool steel OCR12VM with a variety of grinding stone types A46QV and A80LV made of aluminum oxide. The Variables of process parameters are crossfeed (mm / step) and depth of cut (mm). The measurement of vibrations uses an accelerometer, which is processed by the math CAD program in the form of amplitude and frequency. For surface roughness measurements, it is used the MT-301 surface test with 5 sample points and a sample length of 0.8 mm. The results show that the greater the cross-feed value, the bigger the amplitude of the vibration level and the surface roughness of the workpiece. The magnitude of the amplitude of the vibration on the acceleration that occurs in the grinding stone type A46QV starts from 6,7369 -18.7525 g.rms, while the grinding stone type A80LV starts from 5.0904 g.rms to 18.2821 g.rms. The surface roughness achieved in both grit 46 and grit 80 is from N3 to N5.

Application of Improved Grey Prediction Model in Jilin Province of the Software Industry

2014 ◽  
Vol 998-999 ◽  
pp. 1079-1082 ◽  
Author(s):  
Wei Shi Yin ◽  
Pin Chao Meng ◽  
Yan Zhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Software Industry ◽  
Experimental Results ◽  
Jilin Province ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Better Than

Based on the modified grey prediction model, the outputs of software industry in Jilin Province were predicted. First the historical data and updated the data were pre-treated by iteration. Then it was found that the results from the modified grey prediction model were better than that from traditional grey prediction model by residual analysis. Finally, the prediction about the outputs of software industry in Jilin Province was given for the next five years. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.

Influence of Dielectric Jet Flushing during Electro Discharge Diamond Surface Grinding Process

2013 ◽  
Vol 652-654 ◽  
pp. 2187-2190
Author(s):  
Gopal Agarwal ◽  
Manoj Modi
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Electrical Discharge Machine ◽  
Surface Grinding ◽  
Machining Process ◽  
Diamond Surface ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Hybrid Machining Process

The influence of dielectric jet flushing during Electro Discharge Diamond Surface Grinding (EDDSG) on Ti-6A-4V has been reported in this paper. The metal bonded diamond grinding wheel is used as electrode in Electro Discharge Diamond Surface Grinding process. In this process mechanical grinding is coupled with electrical spark of electrical discharge machine to take up the advantages associated with hybrid machining process. The important input parameters in this investigation were “duty factor”, “wheel speed (rpm)”, “magnitude of current (ampere)” and its “duration (Ton, micro-second)”. The effects of these parameters on outcomes i.e. material removal rate (MRR) and surface roughness (Ra) are measured. The noticeable enhancements in material removal rate and surface finish have been seen during EDDSG of Ti-6Al-4V with effective jet flushing. The performance of EDDSG with jet flushing and without jet flushing has been compared.

scholarly journals Optimization of Process Parameter of Surface Grinding Process of Autenitic Stainless Steel (AISI 304) BY Taguchi Method

2019 ◽  
pp. 72-76
Author(s):  
M. A. Deore ◽  
R. S Shelke
Keyword(s):  
Taguchi Method ◽  
Feed Rate ◽  
Metal Removal ◽  
Removal Rate ◽  
Surface Grinding ◽  
Machining Process ◽  
Depth Of Cut ◽  
Work Piece ◽  
Grinding Process ◽  
Machining Processes

The manufacturing process of surface grinding has been established in the mass production of slim, rotationally symmetrical components. Due to the complex set-up, which results from the large sensitivity of this grinding process to a multiplicity of geometrical, kinematical and dynamical influence parameters, surface grinding is rarely applied within limited-lot production. The substantial characteristics of this grinding process are the simultaneous guidance and machining of the work piece on its periphery. Surface grinding is an essential process for final machining of components requiring smooth surfaces and precise tolerances. As compared with other machining processes, grinding is costly operation that should be utilized under optimal conditions. Although widely used in industry, grinding remains perhaps the least understood of all machining processes. The proposed work takes the following input processes parameters namely Work speed, feed rate and depth of cut. The main objective of this work is to predict the grinding behavior and achieve optimal operating processes parameters. a software package may be utilized which integrates these various models to simulate what happens during surface grinding processes. predictions from this simulation will be further analyzed by calibration with actual data. It involves several variables such as depth of cut, work speed, feed rate, chemical composition of work piece, etc. The main objective in any machining process is to maximize the Metal Removal Rate (MRR) and to minimize the surface roughness (Ra). In order to optimize these values Taguchi method, ANOVA and regression analysis is used.

Selection of optimal conditions in the surface grinding process using a differential evolution approach

2007 ◽  
Vol 221 (7) ◽  
pp. 1185-1192 ◽  
Author(s):  
A Gopala Krishna
Keyword(s):  
Differential Evolution ◽  
Production Rate ◽  
Optimization Technique ◽  
Surface Grinding ◽  
Machining Process ◽  
Grinding Process ◽  
Machining Parameters ◽  
Wheel Wear ◽  
Grinding Conditions ◽  
Selection Of

The selection of machining parameters in any machining process significantly affects the production rate, quality, and cost of a component. The present work involves the application of a recently developed global optimization technique called differential evolution to optimize the machining parameters of a surface grinding process. The wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feed rate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size are considered as the process variables. The production cost, production rate, and surface finish are evaluated for the optimal grinding conditions, subject to the constraints of thermal damage, wheel wear parameter, and machine tool stiffness. An example is taken from the literature to compare the results obtained by the proposed approach with other approaches.

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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