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

Creator:

Akshansh Mishra

Date:

2020

Type:

artykuł w czasopiśmie

Description:

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.

Place of publishing:

Koszalin

Publisher:

Publishing House of the Koszalin University of Technology

Format:

application/pdf

Identifier:

oai:dlibra.tu.koszalin.pl:1482

Language:

eng

Is part of:

Journal of Mechanical and Energy Engineering. Vol.4 (44), nr 1, s. 27-32

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY 4.0

Exlibris

Subject and keywords:

local binary patterns friction stir welding machine learning surface defects

Object collections:

Last modified:

Nov 6, 2023

In our library since:

Jun 9, 2022

Number of object content hits:

20

All available object's versions:

https://dlibra.tu.koszalin.pl/publication/1486

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Edition name Date
Akshansh Mishra, 2020, Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy Nov 6, 2023

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E-Mail

dlibra@tu.koszalin.pl

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