Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy

• Autorzy: Mishra Akshansh

Identyfikatory

DOI

10.30464/jmee.2020.4.1.27

• Języki publikacji: EN

Abstrakty

EN

The research reported in this paper focuses on the application of local binary patterns (LBPs) for surface defects detection. The surface defection detection algorithm for friction stir welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the local binary patterns method can be used for real-time surface defects detection in friction stir welded joints.

Słowa kluczowe

EN

local binary patterns; friction stir welding; machine learning; surface defects

PL

lokalne wzorce binarne; zgrzewanie tarciowe z mieszaniem materiału; zgrzewanie tarciowe z przemieszaniem; FSW; uczenie maszynowe; wady powierzchni

• Wydawca: Wydawnictwo Uczelniane Politechniki Koszalińskiej
• Czasopismo: Journal of Mechanical and Energy Engineering
• Rocznik: 2020
• Tom: Vol. 4, No 1
• Strony: 27--32
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Mishra Akshansh

• Stir Research Technologies, Project Scientific Officer, Center for Artificial Intelligence and Friction Stir Welding

Bibliografia

• 1. Kaushik. (2015). A Review on use of Aluminium Alloys in Aircraft Components. i-manager's Journal on Material Science. 3. 33-38
• 2. Çam, Gürel & İpekoğlu, Güven. (2017). Recent developments in joining of aluminum alloys. International Journal of Advanced Manufacturing Technology. 91. 1851-1866. 10.1007/s00170-016-9861-0
• 3. Mishra, R.S. and Ma, Z.Y., 2005. Friction stir welding and processing. Materials science and engineering: R: reports, 50(1-2), pp.1-78
• 4. Meyghani, B.; Awang, M.B.; Emamian, S.S.; Mohd Nor, M.K.B.; Pedapati, S.R. A Comparison of Different Finite Element Methods in the Thermal Analysis of Friction Stir Welding (FSW). Metals 2017, 7, 450
• 5. Kah, P., Rajan, R., Martikainen, J. et al. Investigation of weld defects in friction-stir welding and fusion welding of aluminium alloys. Int J Mech Mater Eng 10, 26 (2015). https://doi.org/10.1186/s40712-015-0053-8
• 6. Brahnam, S., Jain, L.C., Nanni, L. and Lumini, A. eds., 2014. Local binary patterns: new variants and applications (Vol. 2). Berlin: Springer
• 7. Lian, H.C. and Lu, B.L., 2006, May. Multi-view gender classification using local binary patterns and support vector machines. In International Symposium on Neural Networks (pp. 202-209). Springer, Berlin, Heidelberg
• 8. Ahonen, T., Hadid, A. and Pietikainen, M., 2006. Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), pp.2037-2041
• 9. Ojala T, Pietikäinen M, Mäenpää T. Gray scale and rotation invariant texture classification with local binary patterns. In European Conference on Computer Vision 2000 Jun 26 (pp. 404-420). Springer, Berlin, Heidelberg
• 10. Song, K. and Yan, Y., 2013. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 285, pp.858-864
• 11. Ko, J. and Rheem, J., 2016. Defect detection of polycrystalline solar wafers using local binary mean. The International Journal of Advanced Manufacturing Technology, 82(9-12), pp.1753-1764

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).