Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Automated Evaluation of Surface Roughness Using Machine Vision Based Intelligent Systems


Affiliations
1 Department of Mechanical Engineering, MVGRCE(A), Vizianagaram 535 005, Andhra Pradesh, India
2 A U College of Engineering (A), Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
 

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 ever-expanding networking.

Keywords

ANN-PSO, Curvelet Transforms, GLCM, Industry 4.0, RGB, Surface Roughness Evaluation.
User
Notifications
Font Size

  • Zain A M, Haron H & Sharif S, Prediction of surface roughness in the end milling machining using artificial neural network, Expert Syst Appl, 37(2) (2010) 1755–1768, https://doi.org/10.1016/j.eswa.2009.07.033.

  • Samtas G, Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network, Int J Adv Manuf Technol, 73 (2014) 353–364, https://doi.org/10.1007/s00170-014-5828-1.

  • Raju R S & Ramesh R, Image and vibration based mixed variable approach for machining performance estimation, Int J Appl Eng Res, 11(4) (2016) 2646–2650.

  • Palani S & Natarajan U, Prediction and control of surface roughness in cnc end milling by machine vision system using artificial neural network based on 2D fourier transform, Int J Adv Manuf Technol, 54 (2011) 1033–1042, https://doi.org/10.1007/s00170-010-3018-3.

  • Agrawal A , Goel S, Rashid W B & Price M, prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC), Appl Soft Comput, 30 (2015) 279–286, http://dx.doi.org/10.1016/j.asoc.2015.01.059.

  • Nathan D, Thanigaiyarasu G & Vani K, Study on the relationship between surface roughness of AA6061 alloy end milling and image texture features of milled surface, Procedia Eng, 97 (2014) 150–157, https://doi.org/10.1016/j.proeng.2014.12.236.

  • Cuka B, Cho M & Kim D, Vision-based surface roughness evaluation system for end milling, Int J Comput Integ M, 31(2018)727–738, https://doi.org/10.1080/0951192X.2017.1407451.

  • Shahabi H H & Ratnam M M,Simulation and measurement of surface roughness via grey scale image of tool in finish turning, Precis Eng, 43 (2015) 146–153,http://dx.doi.org/10.1016/j.precisioneng.2015.07.004.

  • Srivani A & Anthony Xavior M, Investigation of surface texture using image processing techniques, Procedia Eng, 97 (2014) 1943–1947,https://doi.org/10.1016/j.proeng.2014.12.348.

  • Patwari Md A U, Arif M D, Chowdhury Md S I & Chowdhury Md N A, Identifications of machined surfaces using digital image processing, Int J Eng, X (2012) 213–218.

  • Jurevicius M, Skeivalas J & Urbanavicius R, Analysis of surface roughness parameters digital image identification, Measurement, 56 (2014) 81–87, doi: http://dx.doi.org/10.1016/j.measurement.2014.06.005.

  • HuaianY I, Jian L I U, Enhui L U & Peng A O, Measuring grinding surface roughness based on the sharpness evaluation of colour images, Meas Sci Technol, 27 (2016), http://iopscience.iop.org/0957-0233/27/2/025404.

  • To W, Paul G & Liu D, Surface-type classification using RGB-D, Autom Sci Eng, 11 (2014) 359–366, doi: 10.1109/TASE. 2013.2286354.

  • Zhang Z, Chen Z, Shi J, Jia F & Dai M, Surface roughness vision measurement in different ambient light conditions, Int J Comput Appl Technol, 39(1-3) (2008) 53–57, https://doi.org/10.1109/MMVIP.2008.4749497.

  • Dutta S, Datta A, Das Chakladar N, Pal S K, Mukhopadhyay S & Sen R, Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique, Precis Eng, 36 (2012) 458–466, doi:10.1016/j.precisioneng.2012.02.004.

  • Gadelmawla E S, Estimation of surface roughness for turning operations using image texture features, J Eng Manuf, 225(8) (2011) 1281–1292, doi: 10.1177/ 2041297510393643.

  • Simunovic G, Svalina I, Simunovic K, Saric T, Havrlisan S &Vukelic D, Surface roughness assessing based on digital image features, Adv Prod Eng Manag, 11 (2016) 93–104. http://dx.doi.org/10.14743/apem2016.2.212.

  • Sun W, Yao B,Chen B, He Y, Cao X, Zhou T & Liu H, Noncontact surface roughness estimation using 2D complex wavelet enhanced resnet for intelligent evaluation of milled metal surface quality, Appl Sci, 8 (2018) 381–405, doi:10.3390/app8030381.

  • Pour M, Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform, Int J Adv Manuf Technol, 97 (2018) 2603–2619, https://doi.org/10.1007/ s00170-018-2070-2.

  • Shen L & Yin Q, Texture classification using curvelet transform, Int Symp Info Proc (ISIP’09) (), 2009, 319-324.

  • Raju RS , Raju V R & Ramesh R, Curvelet transform for estimation of machining performance, Optik, 131 (2016) 615–625, http://dx.doi.org/10.1016 /j.ijleo.2016.11.181.

  • Raju R S, Ramesh R, Raju V R & Sharfuddin Md, Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation, J Opt, 47 (2018) 243–250, https://doi.org/10.1007/s12596-018-0457-y.

  • Kaladhar M, Sameer Chakravarthy V S S & Chowdary P S R, Prediction of surface roughness using a novel approach, Int J Ind Eng Prod Res, 32(3) (2021) 1–13, doi:10.22068/ijiepr.32.3.1.

  • Raju R S, Sameer Chakravarthy V S S & Chowdary P S R, Flower pollination algorithm based reverse mapping methodology to ascertain operating parameters for desired surface roughness, Evol Intell, 14 (2021) 1145–1150, https://doi.org/10.1007/s12065-021-00574-1.


Abstract Views: 119

PDF Views: 76




  • Automated Evaluation of Surface Roughness Using Machine Vision Based Intelligent Systems

Abstract Views: 119  |  PDF Views: 76

Authors

Varun Chebrolu
Department of Mechanical Engineering, MVGRCE(A), Vizianagaram 535 005, Andhra Pradesh, India
Ramji Koona
A U College of Engineering (A), Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
R S Umamaheswara Raju
Department of Mechanical Engineering, MVGRCE(A), Vizianagaram 535 005, Andhra Pradesh, India

Abstract


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 ever-expanding networking.

Keywords


ANN-PSO, Curvelet Transforms, GLCM, Industry 4.0, RGB, Surface Roughness Evaluation.

References