Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

• Autorzy: Mishra Akshansh

Identyfikatory

DOI

10.2478/fas-2020-0003

• Języki publikacji: EN

Abstrakty

EN

Friction Stir Welding joint quality depends on input parameters such as tool rotational speed, tool traverse speed, tool tilt angle and an axial force. Surface defects formation occurs when these input parameters are not selected properly. The main objective of the recent paper is to develop Discrete Wavelet Transform algorithm by using Python programming and further subject it to the Friction Stir Welded samples for the identification of various external surface defects present.

Słowa kluczowe

EN

Machine Vision; Surface Defects; Friction Stir Welding; Python programming

• Wydawca: Wydawnictwa Naukowe Sieci Badawczej Łukasiewicz - Instytutu Lotnictwa
• Czasopismo: Fatigue of Aircraft Structures
• Rocznik: 2020
• Tom: Iss. 12
• Strony: 27--35
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wzory

Twórcy

autor

Mishra Akshansh

• Department of Mechanical Engineering, Politecnico Di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy
• Centre for Artificial Intelligent Manufacturing Systems, Neural Net, India

• https://orcid.org/0000-0003-4939-359X

Bibliografia

• [1] 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. 10.1016/j.mser.2005.07.001.
• [2] Thomas, W.M. and Nicholas, E.D. (1997). Friction stir welding for the transportation industries. Materials & Design, 18(4-6), pp. 269-273. 10.1016/s0261-3069(97)00062-9.
• [3] Lohwasser, D. and Chen, Z. eds. (2009). Friction stir welding: From basics to applications. Elsevier.
• [4] Akinlabi, E.T. and Mahamood, R.M. (2020). Introduction to Friction Welding, Friction Stir Welding and Friction Stir Processing. In: Solid-State Welding: Friction and Friction Stir Welding Processes (pp. 1-12). Springer, Cham.
• [5] Kolokas, N., Vafeiadis, T., Ioannidis, D. and Tzovaras, D. (2020). Fault Prognostics in Industrial Domains using Unsupervised Machine Learning Classifiers. Simulation Modelling Practice and Theory, 103, p. 102109. 10.1016/j.simpat.2020.102109
• [6] Aimiyekagbon, O.K., Bender, A. and Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. In Proceedings of the European Conference of the PHM Society, 5(1), pp. 1-11. Available at: www.phmpapers.org/index.php/pheme/issue/view/4
• [7] Mongan, P.G., Hinchy, E.P., O’Dowd, N.P. and McCarthy, C.T., 2020. Optimisation of Ultrasonically Welded Joints through Machine Learning. Procedia CIRP, 93, pp. 527-531. 10.1016/j.procir.2020.04.060.
• [8] Dutt A.K., Sindhuja K., Reddy S.V.N., Kumar P. (2021). Application of Artificial Neural Network to Friction Stir Welding Process of AA7050 Aluminum Alloy. In: Arockiarajan A., Duraiselvam M., Raju R. (eds) Advances in Industrial Automation and Smart Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. 10.1007/978-981-15-4739-3_34.
• [9] Hartl, R., Hansjakob, J. & Zaeh, M.F. (2020). Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization. The International Journal of Advanced Manufacturing Technology, 110, pp. 3145-3167. 10.1007/s00170-020-05696-x.
• [10] Hossam Selim, Fernando Piñal Moctezuma, Miguel Delgado Prieto, José Francisco Trull, Luis Romeral Martínez and Crina Cojocaru (2019). Wavelet Transform Applied to Internal Defect Detection by Means of Laser Ultrasound, Wavelet Transform and Complexity, Dumitru Baleanu, IntechOpen, 10.5772/intechopen.84964. Available from: https://www.intechopen.com/books/wavelet-transform-and-complexity/wavelet-transform-applied-to-internal-defect-detection-by-means-of-laser-ultrasound
• [11] Vermaak, H., Nsengiyumva, P. and Luwes, N. (2016). Using the dual-tree complex wavelet transform for improved fabric defect detection. Journal of Sensors, 2016. 10.1155/2016/9794723.
• [12] Knitter-Piątkowska, A., Guminiak, M.J., Przychodzki, M. (2016). Application of Discrete Wavelet Transformation to Defect Detection in Truss Structures with Rigidly Connected Bars. Engineering Transactions, 64(2,) pp. 157-170. ISSN 2450-8071. Available at: http://www.entra.put.poznan.pl/index.php/et/article/view/319. Date accessed: 01 Nov. 2020.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).