Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Nathan, Diane L.; Thanigaiyarasu, G.; Vani, K.

doi:10.1016/j.proeng.2014.12.236

Cited by 19 publications

(7 citation statements)

References 10 publications

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“…After the capture of the image, it is necessary to improve the quality of the same and the errors of distortion depending on the type of analysis desired. For this purpose, there are some operations of pre-processing such as: morphological, subtraction and filtering [26,27].…”

Section: Image Processing Techniquesmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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Section: Image Processing Techniquesmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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“…The results demonstrated that the highest correlation of investigated features and surface roughness was reached at angles 0° and 180° except for the feature named "correlation" generated from GLCM matrix which had the highest correlation with surface roughness at an angle of 90°. Different from the workpiece inclination (X-Z plane) and workpiece orientation (X-Y plane) authors in the paper [7] investigate direction (0°, 45°, 90° and 135°) of GLCM matrix generation at constant distance between pixels and its influence on the correlation of features extracted from GLCM matrix and surface roughness. Surface texture features based on a digital image GLCM matrix are not used for the surface roughness evaluation and description only.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Correlation between Image Features of Machined Surface and Surface Roughness

et al. 2020

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Alternative approach to surface roughness evaluation is mostly based on the analysis of digital images of machined surfaces i.e. on extracting various features from the matrix mathematically representing a digital image. This paper analyses correlation between 23 different digital image features and the surface roughness for two different materials: aluminium and stainless steel. Machined surfaces for both materials were acquired by face milling. Factorial design 6 × 5 × 2 with two replicates was conducted for each material with cutting parameters being varied on various numbers of levels. Based on the correlation coefficients the results showed that the best ranked features regardless of the machined material were the features based on statistic measures.

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“…Thirteen element feature vector obtained from the captured images from end milled and turning process was used for analysis. The Grey Level Co-occurrence Matrix is utilized to separate the features of the end milled surface texture images by Nathan et al [22].…”

Section: Introductionmentioning

confidence: 99%

Texture classification of machined surfaces using image processing and machine learning techniques

Patel¹,

Vakharia²,

Kiran³

2019

FME Transactions

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The identification of surface texture images from machining surfaces using image processing techniques has been a prominent research area in the recent decades. The aim of this paper is to identify various machined surface texture images using machine learning techniques. Charge coupled device is used to capture images of machined components. Based on captured images, twelve statistical features are extracted and feature vector is formed. Grey Level Co-occurrence Matrix is used to extract statistical features from the machined surface images. Four Machine learning algorithms such as Random Forest, Support Vector Machine, Artificial Neural Network and J48 were utilized to characterize machined surfaces. Training and Tenfold cross validation process is utilized for identification of machined component images. It is found that Artificial Neural Network and Random forest give100 % training accuracy and 99% cross validation accuracy. Results obtained demonstrate the efficiency of proposed methodology, which is useful for identifying texture images.

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Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Cited by 19 publications

References 10 publications

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Investigation of Correlation between Image Features of Machined Surface and Surface Roughness

Texture classification of machined surfaces using image processing and machine learning techniques

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