Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

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.

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

“…The correlation occurs between the tensile strength and the surface appearance and relates to various input and process parameters [67,78,125]. It proves over 95 percent accurate in detecting the good and bad welds, which helps greater in online monitoring and feedback systems [58,103,[131][132][133][134][135][136]. A modified LSVM is also being used to obtain temperature signals of different frequency bands another step towards obtaining more data to check what predicts better weld quality [137][138][139][140][141][142][143][144].…”

Facial Recognition System Using Local Binary Pattern (LBP) Algorithm as a Tool in Curbing Agricultural Loan

“…Whereas, the percentage of successful detection becomes 70.74% during bright lighting conditions, 60.42% for sufficient lighting conditions, and 83.79% for minimum lighting conditions when the ball moves quickly. Mishra (2020) The results showed that the LBP features outperform the classical audio features. It was found that the method is not the cutting edge of the state-of-the-art methods, but it is faster than any convolutional neural network methods and represents a better choice when there is data scarcity or minimal computing power There are several biometrics available like: finger print, iris identification etc.…”

“…Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. There are a lot of research work on using the local binary pattern (LBP) algorithm (Abbasi and Tajeripour, 2017;Wibowo et al, 2019;Mishra, 2020;Toffa and Mignotte, 2020). Abbasi, and Tajeripour, (2017) proposed an automatic method for brain tumor detection in 3D images.…”

The current stage in aerospace at the end of 2020

“…However, things are not so hopeless. The most promising projects in the field of aeronautics are described below, demonstrating that the development of aviation continues (García, 2020;Rana, 2020;Garfo et al, 2020;Kumar & Sreenivasulu, 2019;Mishra & Sarawagi, 2020;Welabo & Tesfamariamr, 2020;Antonescu & Petrescu, 1985;1989;Antonescu et al, 1985a;1985b;1986;1987;1988;1994;1997;2001;Aversa et al, 2017a;2017b;2016d;Ayiei, 2020;Brewer, 1991;Chilukuri et al, 2019;Cao et al, 2013;Dong et al, 2013;Saheed et al, 2019;Riman, 2019;Matthews & Sun Yi, 2019;Dwivedi et al, 2019 a-b;Eremia, 2020;Hanrahan, 2014;He et al, 2013;2008;Hertel, 2017;Komakula, 2019;Langston, 2015Langston, , 2016Lee, 2013;Lin et al, 2013;Liu et al, 2013;Padula & Perdereau, 2013;Perumaal & Jawahar, http://www.ijmp.jor.br v. 13, n. 1, January-march 2022ISSN: 2236…”