Manufacturing Technology 2021, 21(2):270-278 | DOI: 10.21062/mft.2021.029

Application of Edge Detection Technique for Surface Roughness Estimation of Ti-6Al-4V Turned Surfaces

Vishwanatha J. S., Srinivasa Pai P.
Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, India

In this research work, a heuristic method based on biologically motivated Particle Swarm Optimization (PSO) has been proposed for edge detection using multiresolution decomposition, to enhance the quality of the images for predicting surface roughness parameter Ra from Ti-6Al-4V turned surface images. First level Dual Tree Complex Wavelet Transform (DTCWT) is used to decompose the turned images to generate new sub band images. The performance of DTCWT with PSO method is examined for turned surface images and compared with conventional edge detectors like Canny, and Sobel methods along with Discrete Wavelet Transform (DWT) with PSO and DTCWT without edge detection. The obtained results showed that, DTCWT with PSO based edge detection provides better looking edges and also best results are obtained in terms of Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). Further, statistical features have been extracted from the images subjected to proposed edge detection method. The extracted statistical features along with machining parameters and tool flank wear have been given as inputs to radial basis function neural network (RBFNN) to predict Ra of the turned surface images.

Keywords: Surface Roughness, Machine vision, Particle Swarm Optimization (PSO), Dual Tree Complex Wavelet Transform (DTCWT), Radial Basis Function Neural Network (RBFNN).

Received: September 26, 2020; Revised: February 2, 2021; Accepted: February 16, 2021; Prepublished online: March 22, 2021; Published: April 6, 2021  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Vishwanatha JS, Srinivasa Pai P. Application of Edge Detection Technique for Surface Roughness Estimation of Ti-6Al-4V Turned Surfaces. Manufacturing Technology. 2021;21(2):270-278. doi: 10.21062/mft.2021.029.
Download citation

References

  1. EZUGWU, E. O., BONNEY, J. AND YAMANE, Y (2003). An overview of the machinability of aero engine alloys, Journal of materials processing technology, 134(2), pp.233-253 Go to original source...
  2. KAROL VASILKO (2019). Titanium and Technological Problems of Its Machining. Manufacturing Technology, Vol. 19, No. 3, pp.525-530 Go to original source...
  3. GRYNALD'MELLO, P. SRINIVASAPAI, N.P. PUNEET (2017). Optimization studies in high speed turning of Ti-6Al-4V, Applied Soft Computing, 51, pp.105-115 Go to original source...
  4. GÜRCANSAMTAª (2014). Measurement and evaluation of surface roughness based on optic system us-ing image processing and artificial neural network, Int J Adv Manuf Technol, 73, pp.353-364 Go to original source...
  5. SHING I. CHANG, JAYAKUMAR S. RAVATHUR (2005). Computer Vision Based Non-contact Surface Roughness Assessment Using Wavelet Transform and Response Surface Methodology, Quality Engineering, 17 Go to original source...
  6. V. ELANGO, L. KARUNAMOORTHY (2008). Effect of lighting conditions in the study of surface roughness by machine vision-an experimental design approach. Int. J.Adv. Manuf. Technol, 37, pp.92-103 Go to original source...
  7. P. SARMA, S. GHODRATI, S.G. KANDI, M. Mohseni (2015). A histogram-based image processing method for visual and actual roughness prediction of sandpapers, 6thInternational Congress on Color and Coatings 2015, Tehran, Iran, pp.10-12
  8. R.N. SUTTON, E.L. HALL (1972). Texture measures for automatic classification of pulmonary disease, IEEE Trans. Comput. C-21, pp.667-676 Go to original source...
  9. SAJJADGHODRATI, MOHSEN MOHSENI, SAEIDEHGORJI KANDI (2019). Application of image edge detection methods for precise estimation of the standard surface roughnessparameters: Polypropyl-ene/ethylenepropylene- diene-monomer blend as a case study, Measurement, 138, pp.80-90 Go to original source...
  10. M. DORIGO AND S. (2006) Thomas Ant colony optimization, IEEE Computational Intelligence Magazine, 1(4), pp.28-39 Go to original source...
  11. J. KENNEDY, RC EBERHART (2001). Swarm intelligence. Morgan Kaufmann
  12. D. KARABOGA, B. GORKEMLI, C. OZTURK, N. KARABOGA (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), pp.21-57 Go to original source...
  13. GRYNALD'MELLO, SRINIVASAPAI P, PUNEET N. P (2018). Surface Roughness Prediction in High Speed Turning of Ti-6Al-4V: A Comparison of Techniques, Materials Science and Engineering, 376 Go to original source...
  14. A. GROSSMANN, J. MORLET (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15 (4), pp.723-736 Go to original source...
  15. IVAN SELESNICK, RICHARD BARANIUK, N.C. KINGSBURY (2005). The dual-tree complex wavelet transform, IEEE Signal Processing Magazine, 22(6), pp.123 - 151 Go to original source...
  16. N. KINGSBURY (2001). Complex wavelets for shift invariant analysis and filtering of signals (2001) Appl. Comput. Harmon. Anal.,10 (3) Go to original source...
  17. I.W. SELESNICK, R.G. BARANIUK, N.C. KINGSBURY (2005). The dual-tree complex wavelet transform, IEEE Signal Process. Mag., 22 (6), pp.123-151 Go to original source...
  18. BITING YU, BO JIA, LU DING, ZHENGXIANGCAI, QI WU, ROB LAW, JIAYANG HUANG, LEI SONG, SHAN FU (2016). Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion. Neurocomputing, 182, pp.1-9 Go to original source...
  19. KENNEDY, J., EBERHART, R. (1995). Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. Perth Aust., 4, pp.1942-1948 Go to original source...
  20. ALAA ELEYAN, MUHAMMAD S. ANWAR (2017). Multiresolution Edge Detection using Particle Swarm Optimization. International Journal of Engineering Science and Application,1 (1)
  21. BITING YU, BO JIA, LU DING, ZHENGXIANGCAI, QI WU, ROB LAW, JIAYANG HUANG, LEI SONG, SHAN FU (2016). Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion. Neurocomputing, 182, pp.1-9 Go to original source...
  22. VISHWANATHA J S AND SRINIVASAPAI P (2017). DTCWT and GLCM for Surface Roughness Evaluation of Ti-6Al-4V Turned Surface using Computer Vision, Proceedings of International Conference on Manufacturing Technology and Simulation, 2017, IIT Madras, India, pp.134-139
  23. VISHWANATHA J S AND SRINIVASAPAI P (2018) Modeling and prediction of surface roughness in Ti-6Al-4V turned surfaces: use of DTCWT Image Fusion and GLCM, Materials Science and Engineering, 376 Go to original source...
  24. SIMON HAYKIN (2009). Neural networks a comprehensive foundation, second edition
  25. RAVI KEERTHI C, SRINIVASAPAI P, VISHWANATHA J S (2014). Wavelet Transform based Recognition of Machined Surfaces using Computer Vision. Applied Mechanics and Materials, pp.592-594 Go to original source...
  26. M. SONKA, V. HLAVAC, R. BOYLE (2014). Image Processing, Analysis, and Machine Vision, 4th ed., Cengage Learning
  27. S. GHODRATI, S.G. KANDI, M. MOHSENI (2017). Roughness evaluation of randomly roughsurfaces by non-contact image profilometry method, 7th International Congress on Color and Coatings, Tehran, Iran, December, pp.19-21
  28. SAURABH GARG, SURJYA K. PAL, DEBABRATA CHAKRABORTY (2007). Evaluation of the performance of back propagation and radial basis function neural networks in predicting the drill flank wear. Neural Comput & Applic, Vol.16, pp.407-417 Go to original source...
  29. A. ROSENFELD, M. THURSTON (1971). Edge and curve detection for visual scene analysis IEEE Trans. Comput. C-20, pp.562-569 Go to original source...
  30. RIAD HARHOUT, MOHAMED GACEB, SOFIANE HADDAD, SALAH AGUIB, BENATTIA BLOUL, ADELHAMID GUEBLI (2020). Predictive Modelling and Optimisation of Surface Roughness in Turning of AISI1050 Steel Using Polynomial Regression. Manufacturing Technology, Vol. 20, No. 5, pp.591-602 Go to original source...

This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.