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Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems

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Title Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems
 
Creator Chebrolu, Varun
Koona, Ramji
Raju, R S Umamaheswara
 
Subject ANN-PSO
Curvelet transforms
GLCM
Industry 4.0
RGB
Surface roughness evaluation
 
Description 11-25
Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in
the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the
surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of
60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of
machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey
Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to
study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved
lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel
machine vision technique is developed to identify the texture well over the other two extensively researched methods.
Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the
surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based
machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system
based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can
be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information.
One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The
possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of everexpanding
networking.
 
Date 2023-01-16T10:55:52Z
2023-01-16T10:55:52Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61208
https://doi.org/10.56042/jsir.v82i1.69946
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]