Isi Artikel Utama

Akshansh Mishra
Politecnico Di Milano
Italy
Vol. 9 No. 2 (2020), Articles, pages 69-77
DOI: https://doi.org/10.14201/ADCAIJ2020926977
How to Cite

Abstract

Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.

Downloads

Download data is not yet available.

Rincian Artikel

References

Aghdam, S.R., Amid, E. and Imani, M.F., 2012, July. A fast method of steel surface defect detection using decision trees applied to LBP based features. In 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1447-1452). IEEE.

Cai, Y., Xu, G., Li, A. and Wang, X., 2020. A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine. Shock and Vibration, 2020.

Kamani, P., Noursadeghi, E., Afshar, A. and Towhidkhah, F., 2011, November. Automatic paint defect detection and classification of car body. In 2011 7th Iranian Conference on Machine Vision and Image Processing (pp. 1-6). IEEE.

Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L. and He, Y., 2018. Generalized completed local binary patterns for time-efficient steel surface defect classification. IEEE Transactions on Instrumentation and Measurement, 68(3), pp.667-679.

Mahram, A., Shayesteh, M.G. and Jafarpour, S., 2012, July. Classification of wood surface defects with hybrid usage of statistical and textural features. In 2012 35th International Conference on Telecommunications and Signal Processing (TSP) (pp. 749-752). IEEE.

Mijajlovi?M Investigation and Development of Analytical Model for Estimation of Amount of Heat Generated During FSW (in Serbian), Ph. D. thesis, Faculty of Mechanical Engineering Nis, University of Nis, Nis, Serbia, 2012.

Miroslav Mijajlovi? and Dragan Mil?ic (November 21st 2012). Analytical Model for Estimating the Amount of Heat Generated During Friction Stir Welding: Application on Plates Made of Aluminium Alloy 2024 T351, Welding Processes, Radovan Kovacevic, IntechOpen, DOI: 10.5772/53563.

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.

Pratik HS, Vishvesh JB. Friction stir welding of aluminium alloys: An overview of experimental findings – process, variables, development and applications. Proceedings of the Institution of Mechanical Engineers, Part L. 2019;233(6):1191-1226

Rai, R., De, A., Bhadeshia, H.K.D.H. and DebRoy, T., 2011. friction stir welding tools. Science and Technology of welding and Joining, 16(4), pp.325-342.

Ramesh Rudrapati (November 8th 2019). Recent Advances in Joining of Aluminum Alloys by Using Friction Stir Welding, Mass Production Processes, Anil Akdogan and Ali Serdar Vanli, IntechOpen, DOI: 10.5772/intechopen.89382.

Ramona G, Jorge FS. Friction stir welding development of aluminium alloys for structural connections. Proceedings of the Romanian Academy Series A. 2013;14:64-71

Sun, Y., Tsuji, N. and Fujii, H., 2016. Microstructure and mechanical properties of dissimilar friction stir welding between ultrafine grained 1050 and 6061-t6 aluminum alloys. Metals, 6(10), p.249.