Study on the Relationship between Surface Roughness and Texture Features of Engineering Ceramics

2011 ◽  
Vol 697-698 ◽  
pp. 117-120
Author(s):  
Xin Li Tian ◽  
Jian Quan Wang ◽  
Fang Guo
Keyword(s):  
Surface Roughness ◽  
Texture Feature ◽  
Ground Surface ◽  
Texture Features ◽  
Key Factors ◽  
Machined Surface ◽  
Texture Parameters ◽  
Occurrence Matrix ◽  
The Relationship

The surface roughness and surface texture feature both are the key factors to evaluate the ground ceramics surface. The Gray Level Co-occurrence Matrix (GLCM) is introduced to extract and analyze the texture features of ground surface, in order to discover the relationship between texture features and roughness, and predict the roughness of ground ceramics according to the defined texture parameters. Four matrices about different sampling orientations are built up after the sampling offset and total gray levels are determined. By analyzing the correlation about every two features, Contrast, SS and ASM are selected to characterize the texture information of machined surface. Finally, the paper reveals the linear variation laws between Ra, Ry, S, tp and above features. The grounding quality of ceramic products also can be roughly estimated on the ground of that.

Research on Predicting Model of Surface Roughness for Ground Engineering Ceramics

2012 ◽  
Vol 476-478 ◽  
pp. 1036-1040
Author(s):  
Xin Li Tian ◽  
Jian Quan Wang ◽  
Bao Guo Zhang ◽  
Fu Qiang Li
Keyword(s):  
Ground Surface ◽  
Texture Features ◽  
Engineering Ceramics ◽  
Texture Parameters ◽  
Digital Microscope ◽  
Occurrence Matrix ◽  
The Relationship ◽  
Regression Theory ◽  
Surface Images

Grinding is the mostly leading machining technology for engineering ceramics. The quality of ground surface can be evaluated by various roughness parameters. And a textural analysis method based on Gray Level Co-occurrence Matrix was employed in researching the roughness of ground ceramics. The relationship between texture features and roughness was investigated through a series of surface images of engineering ceramics collected by a digital microscope. The sampling offset and total gray levels of surface images were determined firstly. Then 4 GLCMs were built up to calculate the average of texture features. And 6 parameters were fixed as main texture parameters. Furthermore, the multiple nonlinear regression theory was used to assess the relation between the texture features and roughness Ra. By statistic test and comparison, the deviation of calculated Ra and actual Ra is less than 0.25. It is shown that this relation is much satisfactory and the method may be suitable for quickly measuring the roughness of ground ceramics.

scholarly journals Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

2019 ◽  
Vol 15 (03) ◽  
pp. 47 ◽  
Author(s):  
Ann Nosseir ◽  
Seif Eldin A. Ahmed
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
A Value ◽  
Different Types ◽  
Linear Svm ◽  
Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively.  These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying.  To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

scholarly journals Texture Analysis of The Microstructure of Internal Curing Concrete Based On Image Recognition Technology

2021 ◽  
Author(s):  
Lixia Guo ◽  
Weikai Wang ◽  
Ling Zhong ◽  
Lei Guo ◽  
Fangfang Zhang ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Image Recognition ◽  
Texture Features ◽  
Internal Curing ◽  
Effective Parameters ◽  
Texture Parameter ◽  
Texture Parameters ◽  
Occurrence Matrix ◽  
Cementing Material ◽  
The Relationship

Abstract Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the characteristics of materials and the sensitivity of parameters, not all Tamura and GLCM texture parameters can effectively characterize the texture features of internal curing concrete materials. In terms of the Tamura texture, coarseness, regularity, and directionality are effective parameters to predict the compressive strength of the internal curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.

Texture Analysis of Turned Surface Images Using Grey Level Co-Occurrence Technique

2011 ◽  
Vol 365 ◽  
pp. 38-43 ◽  
Author(s):  
Anurup Datta ◽  
Samik Dutta ◽  
Surjya K. Pal ◽  
Ranjan Sen ◽  
Sudipta Mukhopadhyay
Keyword(s):  
Surface Roughness ◽  
Texture Analysis ◽  
Image Texture ◽  
Analysis Method ◽  
Grey Level ◽  
Machined Surface ◽  
Image Texture Analysis ◽  
Pixel Pair ◽  
Occurrence Matrix

The main purpose of this work was to study the applicability of an image texture analysis method, namely, the grey level co-occurrence matrix (GLCM) method for the examination of the smoothness of the images of a turned surface. The effect of the variation of the pixel pair spacing (pps) on the construction of the GLCM was also considered and then, contrast and homogeneity were calculated from the GLCMs which served as texture descriptors for the quality of the machined surface. Finally, the variation of these texture descriptors with cutting time was analyzed and compared with the variation of tool wear and surface roughness with cutting time.

Effects on Surface Integrity of Ti6Al4V in High Speed Milling

2017 ◽  
Vol 737 ◽  
pp. 156-161 ◽  
Author(s):  
Yong Feng Fang ◽  
Kong Fah Tee
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
High Speed ◽  
Work Piece ◽  
Machined Surface ◽  
High Speed Milling ◽  
Edge Radius ◽  
Roughness Model ◽  
The Relationship

Surface topography is a significant factor that affects directly the surface integrity. There are several influencing factors. The purpose of the study is to investigate the effects of edge radius on surface integrity of Ti6Al4V. The proposed approach uses three different angles to study the relationship between the edge radius and surface roughness. The study develops theoretical model, roughness model based on cutting force and roughness empirical model. Experimental results show that machined surface integrity of TC4 is sensitive to the variations of the edge radius. The method is effective and can provide a guidance to optimize edge radius. It has realized higher accurate prediction of surface integrality in precision high speed milling with one of the models and has improved surface roughness quality of the work-piece.

Experimental Research on the Relationship between Surface Polishing and Fracture Strength for Ground Ceramics

2013 ◽  
Vol 770 ◽  
pp. 433-436
Author(s):  
Xin Li Tian ◽  
Jian Quan Wang ◽  
Bao Guo Zhang ◽  
Peng Xiao Wang
Keyword(s):  
Residual Stress ◽  
Fracture Strength ◽  
Ground Surface ◽  
Residual Tensile Stress ◽  
Residual Stress Field ◽  
Polishing Process ◽  
Machined Surface ◽  
X Ray ◽  
Surface Polishing ◽  
The Relationship

Fracture strength is one of the key mechanics performances for engineering ceramics products, greatly influenced by the microscopic topography and residual stress field of ground surface. In this work, several testing equipments, such as the metallurgical microscope, surface profiler and X ray residual stress tester were introduced to investigate the relationships between microscopic topography, surface roughness, residual stress and fracture strength of ground ceramics, after the surface grinding and mechanical polishing. The experimental results show that a smoother machined surface with low roughness and residual stress is obtained through polishing with absolute alcohol for 20 minutes; the fracture strength of Si3N4SiC and Al2O3 are increased by 6.64%8.18% and 6.58% respectively, comparing to the ceramics without polishing; the surface stress concentration and residual tensile stress of polished ceramics are both reduced after an appropriate time of polishing process, which causes a certain improvement of ground fracture strength.

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

Yarn and fabric properties in a modified ring spinning system considering the effect of the friction surface of the false-twister

2019 ◽  
Vol 90 (5-6) ◽  
pp. 572-580 ◽  
Author(s):  
Rong Yin ◽  
Xiao-Ming Tao ◽  
Bin-gang Xu
Keyword(s):  
Surface Roughness ◽  
Friction Surface ◽  
Experimental Results ◽  
Material Surface ◽  
Ring Spinning ◽  
Knitted Fabrics ◽  
Yarn Properties ◽  
Cotton Yarns ◽  
The Relationship

This paper experimentally studies the relationship between the friction surface of a false-twisting unit and the quality of cotton yarns produced by a modified ring spinning system, with the adoption of the single friction-belt false-twister. The friction surface of the false-twisting unit, as a key twisting component, has been studied in terms of material, surface roughness, hardness and diameter, as well as the interaction between these factors and resultant yarn properties, with particular attention to yarn imperfections. Experimental results showed that the false-twisting unit with a short interactive path demonstrated significant reduction of yarn imperfections, especially yarn neps. With the optimal false-twisting unit, performances of the modified yarns and their knitted fabrics were evaluated and compared with the conventional ones.

ANALYSIS OF THE CORRELATION BETWEEN FRACTAL STRUCTURE OF CUTTING FORCE SIGNAL AND SURFACE ROUGHNESS OF MACHINED WORKPIECE IN END MILLING OPERATION

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950013 ◽  
Author(s):  
AHMAD THUFFAIL THASTHAKEER ◽  
ALI AKHAVAN FARID ◽  
CHANG TECK SENG ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Surface Quality ◽  
Cutting Forces ◽  
End Milling ◽  
Complex Structure ◽  
Machined Surface ◽  
End Mills ◽  
Machining Operations

Analysis of the machined surface is one of the major issues in machining operations. On the other hand, investigating about the variations of cutting forces in machining operation has great importance. Since variations of cutting forces affect the surface quality of machined workpiece, therefore, analysis of the correlation between cutting forces and surface roughness of machined workpiece is very important. In this paper, we employ fractal analysis in order to investigate about the complex structure of cutting forces and relate them to the surface quality of machined workpiece. The experiments have been conducted in different conditions that were selected based on cutting depths, type of cutting tool (serrated versus. square end mills) and machining conditions (wet and dry machining). The result of analysis showed that among all comparisons, we could only see the correlation between complex structure of cutting force and the surface roughness of machined workpiece in case of using serrated end mill in wet machining condition. The employed methodology in this research can be widely applied to other types of machining operations to analyze the effect of variations of different parameters on variability of cutting forces and surface roughness of machined workpiece and then investigate about their correlation.

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