Surface finish evaluation using curvelet transforms based machine vision system

Machining is done to obtain dimensional accuracy and surface finish; several automated systems are available for the evaluation of dimensional accuracy, whereas surface finish evaluation systems are rare. Face milling operation is performed at diverse cutting parameters (speed, feed rate, and depth...

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Published inMaterials today : proceedings Vol. 44; pp. 500 - 505
Main Authors Bharat, G.C.S.G., Umamaheswara Raju, R.S., Srinivas, B.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2021
Subjects
ANN-PSO
Curvelet Transforms
Machine vision system
Surface finish evaluation
Texture features
Machine vision system
Curvelet Transforms
Texture features
Surface finish evaluation
ANN-PSO
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Abstract Machining is done to obtain dimensional accuracy and surface finish; several automated systems are available for the evaluation of dimensional accuracy, whereas surface finish evaluation systems are rare. Face milling operation is performed at diverse cutting parameters (speed, feed rate, and depth of cut) on aluminum 6101 alloys. In this work, a machine vision system is developed for the evaluation of surface finish. Curvelet transforms based advanced image processing techniques are used to extract texture features from machined surface captured images. An ANN-PSO model is developed to map the texture feature and the measured surface finish. The model evaluated the surface finish accurately for given texture features. Machine vision systems as such are non-tangible; scratch protective, less time consuming, cost-effective, and productive.
AbstractList Machining is done to obtain dimensional accuracy and surface finish; several automated systems are available for the evaluation of dimensional accuracy, whereas surface finish evaluation systems are rare. Face milling operation is performed at diverse cutting parameters (speed, feed rate, and depth of cut) on aluminum 6101 alloys. In this work, a machine vision system is developed for the evaluation of surface finish. Curvelet transforms based advanced image processing techniques are used to extract texture features from machined surface captured images. An ANN-PSO model is developed to map the texture feature and the measured surface finish. The model evaluated the surface finish accurately for given texture features. Machine vision systems as such are non-tangible; scratch protective, less time consuming, cost-effective, and productive.
Author Umamaheswara Raju, R.S.
Srinivas, B.
Bharat, G.C.S.G.
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Cites_doi 10.1016/j.ymssp.2017.05.006
10.1007/s00170-018-2070-2
10.1007/s12596-018-0457-y
10.1007/s00170-010-3018-3
10.1016/j.jksuci.2018.11.006
10.1016/j.fuel.2019.02.001
10.1016/j.ymssp.2017.11.022
10.1016/j.ijleo.2016.11.181
10.1016/j.eswa.2010.11.041
10.1007/s10845-017-1381-8
10.1109/ICMCCE.2018.00062
10.1016/j.triboint.2009.05.030
10.1007/978-981-13-1724-8_14
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Keywords Machine vision system
Curvelet Transforms
Texture features
Surface finish evaluation
ANN-PSO
Language English
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References B. Dhanasekar, B. Ramamoorthy Tribolo Restoration of blurred images for surface finish evaluation using machine vision. International, Volume 43, Issues 1–2, January February 2010, Pages 268–276.
Kavya kangaraj, G.G.Lakshmi Priya, Curvelet transform based feature extraction and selection for multimedia event classification, Journal of King Saud University, In press, 23 November 2018
Ilhan Asilttirka, Mehmet cunka, Modeling and prediction of surface finish in turning operations using artificial neural network and multiple regression method, May 2011, Pages 5826- 5832, Volume 38, Issue 5, Expert Systems with Applications.
E. García Plaza, P.J. Núñez López, Analysis of cutting force signals by wavelet packet transform for surface finish monitoring in CNC turning, 2017.
Li Zhou,Xiaopeng Zhuang,Hanzhang Liu and Dawei Liu, Study on brittle graphite surface roughness detection based on gray-level-co-occurrence matrix.. 2018 3rd International Conference on Mechanical, Control and Computer Engineering. Pages 273-276, 2018.
ShihuZhao, YongLi, Yanbin, Wang, ZhentaoMa, XiaoqiangHuang, Quantitative study on coal and shale pore structure and surface roughness based on atomic force microscopy and image processing, Fuel, Volume 244, 15 May 2019, Pages 78-90.
Mikołajczyk, Nowicki, Bustillo, Yu Pimenov (b0065) 2018; 104
Umamaheswara Raju, Ramachandra Raju, Ramesh (b0040) 2017; 131
R. S. Umamaheswara Raju, R. Ramesh V. Ramachandra Raju, Sharfuddin Mohammad, Curvelet transforms and flower pollination algorithm-based machine vision system for finish estimation, Journal of Optics 47 (2), 243-250., The Optical Society of India 2018.
Weifang Sun, Bin Yao, Binqiang Chen, Yuchao He, Xincheng Cao, Tianxiang Zhou, and Huang Liu, Noncontact Surface Finish Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality, 2018.
Liran Shen, and Qingbo Yin, “Texture Classification using Curvelet Transform”, Proceedings of the 2009 International Symposium on Information Processing (ISIP’09)
S. Palani U. Natarajan , Prediction of surface finish in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. The International Journal of Advanced Manufacturing Technology, June 2011, Volume 54, Issue 9, pp 1033–1042 First online: 25 November 2010.
D.Yu.Pimenov, A. Bustillo, T. Mikołajczyk, Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth, Journal of intelligent manufacturing, 29, Pages1045–1061(2018).
Masoud Pour, Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform, The International Journal of Advanced Manufacturing Technology, Volume 97, pages 2603-2619(2018)
J. Mahashar Ali, H. Siddhi Jailani, M. Murugan, Surface Roughness Evaluation of Milled Surfaces by Image Processing of Speckle and White-Light Images, Advances in manufacturing processes Page:141-151, 11 September 2018.
10.1016/j.matpr.2020.10.203_b0005
10.1016/j.matpr.2020.10.203_b0045
10.1016/j.matpr.2020.10.203_b0035
10.1016/j.matpr.2020.10.203_b0025
10.1016/j.matpr.2020.10.203_b0015
10.1016/j.matpr.2020.10.203_b0030
10.1016/j.matpr.2020.10.203_b0020
10.1016/j.matpr.2020.10.203_b0075
10.1016/j.matpr.2020.10.203_b0010
10.1016/j.matpr.2020.10.203_b0055
10.1016/j.matpr.2020.10.203_b0070
10.1016/j.matpr.2020.10.203_b0060
10.1016/j.matpr.2020.10.203_b0050
Mikołajczyk (10.1016/j.matpr.2020.10.203_b0065) 2018; 104
Umamaheswara Raju (10.1016/j.matpr.2020.10.203_b0040) 2017; 131
References_xml – volume: 131
  start-page: 615
  year: 2017
  end-page: 625
  ident: b0040
  article-title: Curvelet transform for the estimation of machining performance
  publication-title: Optik
  contributor:
    fullname: Ramesh
– volume: 104
  start-page: 503
  year: 2018
  end-page: 513
  ident: b0065
  article-title: Predicting tool life in turning operations using neural networks and image processing
  publication-title: Mech. Syst. Sig. Process.
  contributor:
    fullname: Yu Pimenov
– ident: 10.1016/j.matpr.2020.10.203_b0020
  doi: 10.1016/j.ymssp.2017.05.006
– ident: 10.1016/j.matpr.2020.10.203_b0035
– ident: 10.1016/j.matpr.2020.10.203_b0030
  doi: 10.1007/s00170-018-2070-2
– ident: 10.1016/j.matpr.2020.10.203_b0045
  doi: 10.1007/s12596-018-0457-y
– ident: 10.1016/j.matpr.2020.10.203_b0005
  doi: 10.1007/s00170-010-3018-3
– ident: 10.1016/j.matpr.2020.10.203_b0050
  doi: 10.1016/j.jksuci.2018.11.006
– ident: 10.1016/j.matpr.2020.10.203_b0055
  doi: 10.1016/j.fuel.2019.02.001
– volume: 104
  start-page: 503
  issue: 1
  year: 2018
  ident: 10.1016/j.matpr.2020.10.203_b0065
  article-title: Predicting tool life in turning operations using neural networks and image processing
  publication-title: Mech. Syst. Sig. Process.
  doi: 10.1016/j.ymssp.2017.11.022
  contributor:
    fullname: Mikołajczyk
– volume: 131
  start-page: 615
  year: 2017
  ident: 10.1016/j.matpr.2020.10.203_b0040
  article-title: Curvelet transform for the estimation of machining performance
  publication-title: Optik
  doi: 10.1016/j.ijleo.2016.11.181
  contributor:
    fullname: Umamaheswara Raju
– ident: 10.1016/j.matpr.2020.10.203_b0025
– ident: 10.1016/j.matpr.2020.10.203_b0015
  doi: 10.1016/j.eswa.2010.11.041
– ident: 10.1016/j.matpr.2020.10.203_b0075
  doi: 10.1007/s10845-017-1381-8
– ident: 10.1016/j.matpr.2020.10.203_b0070
  doi: 10.1109/ICMCCE.2018.00062
– ident: 10.1016/j.matpr.2020.10.203_b0010
  doi: 10.1016/j.triboint.2009.05.030
– ident: 10.1016/j.matpr.2020.10.203_b0060
  doi: 10.1007/978-981-13-1724-8_14
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SubjectTerms ANN-PSO
Curvelet Transforms
Machine vision system
Surface finish evaluation
Texture features
Title Surface finish evaluation using curvelet transforms based machine vision system
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