Evaluation of 3D Surface Roughness Parameters of EDM Components Using Vision System

Shivanna, D.M.; Kiran, M.B.; Kavitha, S.

doi:10.1016/j.mspro.2014.07.416

Cited by 27 publications

(12 citation statements)

References 6 publications

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“…The average surface roughness (Ra) is generally used as a dimensional index to determine the surface finish of a machined surface . Assessment of roughness parameters plays a vital role to distinguish problems such as friction, contact deformation, and tightness of contact joint accuracy in industrial sectors …”

Section: Methodsmentioning

confidence: 99%

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Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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Section: Methodsmentioning

confidence: 99%

“…25 Assessment of roughness parameters plays a vital role to distinguish problems such as friction, contact deformation, and tightness of contact joint accuracy in industrial sectors. 26…”

Section: Methodsmentioning

confidence: 99%

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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“…The process of inspection by vision may be characterized by 4 steps, that is: Capturing the Image, Image Analysis, Getting Results and Determine the system action (pass or fail) [5].…”

Section: Image Processing Techniquesmentioning

confidence: 99%

“…The proposed solution to this type of problem is to implement a vision system for inspection in the same place of production (on site), which operates as a Poka Yoke. This technique allows you to prevent any defective product to go on to the next operation, with which you can have a 100% inspection without the intervention of the human being [5].…”

Section: Introductionmentioning

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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“…Shivanna et al [11] proposed a method to evaluate surface roughness parameters of EDM components using machine vision. Images are captured using Charge Coupled Device (CCD) camera and transferred to computer using frame grabber.…”

Section: Surface Qualitymentioning

confidence: 99%

A Review on Pattern Recognition of Components for the Quality evaluation using Machine Vision

Loyola¹

2017

IJETED

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Automatic inspection of pattern of the components in the industry using computer vision and image processing techniques are capable of accurately measuring the quality parameters and effectively monitor the pattern/shape of the component. There has been significant development in the field of machine vision and is widely accepted by all the industries for quality inspection . The usage of machine vision can replace the manual and traditional method of quality inspection.This paper provides review of the computer vision techniques and their applications for visual inspection and decision making in the field of quality inspection in the manufacturing industries.

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Evaluation of 3D Surface Roughness Parameters of EDM Components Using Vision System

Cited by 27 publications

References 6 publications

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

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

A Review on Pattern Recognition of Components for the Quality evaluation using Machine Vision

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