48
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Characterisation of friction stir welds by logistic regression using fractal and wavelet features

ORCID Icon, , , & ORCID Icon
Pages 582-597 | Accepted 04 Jul 2019, Published online: 16 Jul 2019

References

  • Maria Asli Sicilan T Dr, Senthil Kumar S. Analysis of surface quality of friction stir welding joints using image processing techniques. Proceedings of International Conference on Emerging Trends in Engineering & Technology, Kollam, Kerala,  Volume: 1, March2014.
  • Mallieswaran K, Padmanabhan R, Balasubramanian V. Friction stir welding parameters optimization for tailor welded blank sheets of AA1100 with AA6061 dissimilar alloy using response surface methodology. Adv Mater Process Technol. 2018;4(1):142–157.
  • Wahab MA, Dewan MW, Huggett DJ, et al. Challenges in the detection of weld-defects in friction-stir-welds (FSW). Adv Mater Process Technol. 2019;5(2):258–278.
  • Nejatpour R, Sadabad AA, Mashhad I, et al. Automated weld defects detection using image processing and CAD methods. Proceedings of IMECE2008 ASME International Mechanical Engineering Congress and Exposition; October 31–November 6, 2008, Boston, Massachusetts, USA pp. 979–987. Vol. 11.
  • Mohan A, Poobal S. Crack detection using image processing: A critical review and analysis. Alexandria Eng J. 2017. (in press). DOI:10.1016/j.aej.2017.01.020,
  • Hassan J, Awan AM, Jalil A. Welding defect detection and classification using geometric features. 10th International Conference on Frontiers of Information Technology; Islamabad, Pakistan 2012. pp. 139–144.
  • Parikha C, Ranjanb R, Khanc AR, et al. Volumetric defect analysis in friction stir welding based on three dimensional reconstructed images. J Manuf Processes. 2017;pp.29:96, Vol.112.
  • Das B, Bag S, Pal SS. Defect detection in friction stir welding process through characterization of signals by fractal dimension. Manuf Lett. 2016;7:6–10.
  • Ranjan R, Khan AR, Parikh C, et al. Classification and identification of surface defects in friction stir welding: an image processing approach. J Manuf Processes. 2016;22:237–253.
  • xiao-guang Z, jian-Jan X, yu L. The research of defect recognition for radiographic weld image based on fuzzy neural network, Proceedings of the 5th World Congress on Intelligent Control and Automation; Hangzhou, China, 2004. pp. 2661–2665. Vol. 3.
  • Megahed M, Camelio JA. Real-time fault detection in manufacturing environments using face recognition techniques. J Intell Manuf. 2012;23(3):393–408.
  • Meakin P, Stanley HE, Coniglio A, et al. Surfaces, interfaces, and screening of fractal structures. Phys Rev A. 1985;32(4):2364.
  • Yong X, Qiang C, Zhenguo: S. Application of fractal theory in welding image processing. J Image Graphics. 2002;7:86–90.
  • Hsuan Tien lin. Texture classification using fractal based features and support vector machines, Report; http://www.work.caltech.edu/~htlin/course/ 2004.
  • Moisy F. Boxcount. MATLAB Central; Mathworks, 2006.
  • Gould DJ, Vadakkan TJ, Poché RA, et al. Multifractal and lacunarity analysis of microvascular morphology and remodeling. Microcirculation (New York, NY : 1994). 2011;18:136–151.
  • Borys P. On the relation between lacunarity and fractal dimension. Acta Phys Pol B. 2009;40(5):1485–1490.
  • Tolle CR, McJunkin TR, Gorsich DJ. An efficient implementation of the gliding box lacunarity algorithm. Phys D. 2008;237(3):306–315.
  • Vadakkan T. Lacunarity of a binary image. MATLAB Central; Mathworks, 2009.
  • Hiremath PS, Shivashankar S. Wavelet based features for texture classification. Gvip J. 2006;6(3):55–58.
  • Hiremath PS, Shivashankar S. Texture classification using wavelet packet decomposition. Gvip J. 2006;6(2):77–80.
  • Sebe N, Lew MS, Wavelet based texture classification. Proc. international conference on pattern recognition; Barcelona, Spain, 2000. pp. 959–962. Vol. 3.
  • Yu G, Lin Y, Kamarthi S. Wavelets-based feature extraction for texture classification. Adv Mater Res. 2010;97–101:1273–1276.
  • Doshi DA, Kothari AM, Kamdar DG. Feature extraction for texture classification–an approach with discrete wavelet transform. Int J Darshan Inst Eng Res Emerg Technol. 2013;2:6–10.
  • Das B, Pal S, Bag S. Monitoring of friction stir welding process using weld image information. Sci Tec Weld Joining. 2016;21(4):317–324.
  • Bhat NN, Kumari K, Dutta S, et al. Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Processes. 2015;20:274–281.
  • Coifman RR, Wickerhauser MV. Entropy-based algorithms for best basis selection. IEEE Trans Inform Theory. 1992 Mar;38:713–718.
  • Pun C-M, Lee M-C. Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell. 2003 May;25(5):590–603.
  • Yu J. Quality estimation of resistance spot weld based on logistic regression analysis of welding power signal. Int J Precis Eng Manuf. 2015;16:2655–2663.
  • Available from: www.coursera.org/learn/machine-learning; 2017.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.