Prediction of Surface Quality for Silicon Carbide Wheel Grinding of Silicon Nitride

2006 ◽  
Vol 532-533 ◽  
pp. 297-300
Author(s):  
Li Ming Xu ◽  
Albert J. Shih ◽  
Bin Shen ◽  
Chun Xiang Ma ◽  
De Jin Hu
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
Silicon Nitride ◽  
Surface Quality ◽  
Prediction Accuracy ◽  
Surface Damage ◽  
Small Samples ◽  
Support Vector ◽  
Grinding Parameters ◽  
Grinding Efficiency

The experimental results for silicon carbide (SiC) wheel with fine grit size grinding of silicon carbide (Si3N4) revealed that the grinding parameters affect not only the ground silicon nitride surface roughness, but also the degree of surface damage. There exists complex non-linear relationship between the grinding parameters and surface quality. Better surface roughness doesn’t surely mean less surface damage. A method of prediction of grinding quality based on support vector regression is then presented according to the condition of small samples. The result shows the prediction accuracy based on this method is obviously higher than neural network, which provides an effective way for optimizing the grinding parameters to ensure the grinding quality as well as grinding efficiency while grinding of silicon carbide using conventional abrasive.

Optimization of Coated SiC Belt Grinding of Alumina Ceramics

2009 ◽  
Vol 76-78 ◽  
pp. 38-42 ◽  
Author(s):  
Xavier Kennedy ◽  
S. Gowri
Keyword(s):  
Surface Roughness ◽  
Material Removal ◽  
High Hardness ◽  
Surface Damage ◽  
High Temperature Strength ◽  
Structural Ceramics ◽  
Alumina Ceramics ◽  
Belt Grinding ◽  
Grinding Parameters ◽  
Surface Damages

Advanced structural ceramics have been increasingly used in automotive, aerospace, military, medical and other applications due to their high temperature strength, low density, thermal and chemical stability. However, the Grinding of advanced ceramics such as alumina is difficult due to its low fracture toughness and sensitivity to cracking, high hardness and brittleness. In this paper, surface integrity and material removal mechanisms of Alumina ceramics ground with SiC abrasive belts, have been investigated. The surface damage have been studied with scanning electron microscope (SEM). The significance of grinding parameters on the responses was evaluated using Signal to Noise ratios.This research links the surface roughness and surface damages to grinding parameters. The optimum levels for maximum material removal and surface roughness been discussed.

Effects of grinding parameters on surface quality in silicon nitride grinding

2017 ◽  
Vol 43 (1) ◽  
pp. 1571-1577 ◽  
Author(s):  
Wei Liu ◽  
Zhaohui Deng ◽  
Yuanyuan Shang ◽  
Linlin Wan
Keyword(s):  
Silicon Nitride ◽  
Surface Quality ◽  
Grinding Parameters

Diamond Coated End Mills in Machining Silicon Carbide

2012 ◽  
Vol 576 ◽  
pp. 531-534 ◽  
Author(s):  
Mohamed Konneh ◽  
Mohammad Iqbal ◽  
Nik Mohd Azwan Faiz
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
High Performance ◽  
Removal Rate ◽  
Roughness Parameter ◽  
Surface Damage ◽  
Ceramic Materials ◽  
Real Problem ◽  
Machining Parameters ◽  
Machining Processes

Silicon Carbide (SiC) is a type of ceramic that belongs to the class of hard and brittle material. Machining of ceramic materials can result in surface alterations including rough surface, cracks, subsurface damage and residual stresses. Efficient milling of high performance ceramic involves the selection of appropriate operating parameters to maximize the material removal rate (MRR) while maintaining the low surface finish and limiting surface damage. SiC being a ceramic material, its machining poses a real problem due to its low fracture toughness, making it very sensitive to crack. The paper discusses milling of silicon carbide using diamond coated end mill under different machining conditions in order to determine the surface roughness parameter, Rt after the machining processes and to establish a relationship between the machining parameters and response variables. Based on the surface roughness carried out the lowest Rt obtained is 0.46 µm.

scholarly journals Experimental Optimization of Annular Polishing Parameters for Silicon Carbide

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yuan Liu ◽  
La Han ◽  
Haiying Liu ◽  
Yikai Shi ◽  
Junjie Zhang
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
Surface Quality ◽  
Material Removal ◽  
Polished Surface ◽  
Strong Impact ◽  
Machined Surface ◽  
Experimental Optimization ◽  
Machined Surface Quality ◽  
Influential Parameters

Machined surface quality has a strong impact on the functionality of silicon carbide-based components and devices. In the present work, we first analytically investigate the complex coupling of motions in annular polishing based on the Preston equation, which derives the influential parameters for material removal. Subsequently, we conduct systematic annular polishing experiments of reaction-bonded silicon carbide to investigate the influence of derived parameters on polished surface quality, which yield optimized polishing parameters for achieving ultralow surface roughness of reaction-bonded silicon carbide.

A Study on Grinding Parameter Optimization of Reaction Bonded Silicon Carbide

2013 ◽  
Vol 631-632 ◽  
pp. 660-665 ◽  
Author(s):  
Yao Wang ◽  
Zha Yan Feng
Keyword(s):  
Residual Stress ◽  
Surface Roughness ◽  
Silicon Carbide ◽  
Residual Stresses ◽  
Compressive Residual Stress ◽  
Bending Strength ◽  
Grinding Parameters ◽  
Depth Analysis ◽  
Cut Depth ◽  
Full Factorial

In order to enhance the efficiency and the surface smooth degree of the RBSiC grinding, a three factors two levels full factorial design was utilized to optimize the process. Combined with the effects of grinding parameters on surface roughness, the grit cut depth analysis was employed to choose the appropriate grinding parameters. The strength reliability and the residual stresses of the RBSiC ground using the optimized parameters were investigated. The results show that comparing to the polished RBSiC the ground ones have higher compressive residual stress, lower crack scatter and similar average bending strength.

Effects of Electricity Parameters on Surface Quality of Reaction Bonded Silicon Carbide Ceramic with Electrical Discharge Diamond Grinding

2016 ◽  
Vol 874 ◽  
pp. 15-21 ◽  
Author(s):  
Xiao Shuang Rao ◽  
Fei Hu Zhang ◽  
Chen Li
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
Surface Quality ◽  
Electrical Discharge ◽  
Machining Process ◽  
Hybrid Machining ◽  
Micro Cracks ◽  
Sic Ceramic ◽  
Hybrid Machining Process

With some conductivity and low grinding affectivity, a hybrid machining process termed electrical discharge diamond grinding (EDDG) is applied to the precision machining of reaction bonded silicon carbide (RB-SiC) ceramic. As there is electrical spark in the hybrid machining process, the electrical parameters are varied to explore their effects on the surface quality of RB-SiC ceramic with EDDG. In this paper, the experiments of different polarity and gap voltage with EEDG were investigated, and the microstructure and surface roughness on the machined surface of RB-SiC ceramic were analyzed. The surface morphology and micro-cracks were examined with a scanning electron microscope, and the surface roughness was measured with a confocal scanning laser microscope. It is found that surface roughness initially increases and then decreases with increase of the gap voltages and is higher with negative polarity than that with positive polarity. The micromorphology Micro-cracks were observed on the surface machined and are outstanding in re-solidified zone with EDDG.

Improving the Surface Roughness of a CVD Coated Silicon Carbide Disk by Performing Ductile Regime Single Point Diamond Turning

2008 ◽  
Author(s):  
Deepak Ravindra ◽  
John Patten
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
Surface Quality ◽  
Removal Rate ◽  
Single Point ◽  
Extreme Environments ◽  
High Hardness ◽  
Diamond Turning ◽  
Ductile Regime Machining

Silicon carbide (SiC) is one of the advanced engineered ceramics materials designed to operate in extreme environments. One of the main reasons for the choice of this material is due to its excellent electrical, mechanical and optical properties that benefit the semiconductor, MEMS and optoelectronic industry respectively. Manufacture of this material is extremely challenging due to its high hardness, brittle characteristics and poor machinability. Severe fracture can result when trying to machine SiC due to its low fracture toughness. However, from past experience it has been proven that ductile regime machining of silicon carbide is possible. The main goal of the subject research is to improve the surface quality of a chemically vapor deposited (CVD) polycrystalline SiC material to be used in an optics device such as a mirror. Besides improving the surface roughness of the material, the research also emphasized increasing the material removal rate (MRR) and minimizing the diamond tool wear. The surface quality was improved using a Single Point Diamond Turning (SPDT) machining operation from 1158nm to 88nm (Ra) and from 8.49μm to 0.53μm (Rz; peak-to-valley).

Effects analysis of processing factors on surface quality in optical grinding using ɛ-support vector regression

2015 ◽  
Vol 229 (18) ◽  
pp. 3449-3458 ◽  
Author(s):  
Dongxu Zhang ◽  
Ping Yang ◽  
Yanting Zhang ◽  
Guo Bi ◽  
Yinbiao Guo
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Support Vector Regression ◽  
Surface Quality ◽  
Processing Condition ◽  
Surface Shape ◽  
Support Vector ◽  
Technological Parameters ◽  
Effect Factor ◽  
Processing Factors

The aim of this study was to quantitatively analyze the effects of the processing factors on the surface quality in precision optical grinding. A novel identifying model which incorporates an effect factor is proposed based on ɛ-support vector regression ( ɛ-SVR). Experiments were designed and performed to investigate the effects of the processing factors comprising the technological parameters and processing condition factors on the surface quality, and the experimental data were used to train the ɛ-SVR. Subsequently, the values of effect factor were solved to quantify the effects of the respective processing factors on the surface quality. Further experiments were performed to verify the effectiveness of effect factor. ɛ-SVRs of which the input vectors were multiplied and not multiplied by effect factor were respectively used to predict the surface quality including the surface roughness and surface shape peak–valley value. The values calculated by ɛ-SVR using effect factor were found to be much more accurate than those calculated without using effect factor. The results confirmed the effectiveness of identifying model for precision optical grinding.

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