Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system

Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system

This research is carried out be able to automatically identify the joint position and classify the quality level of imperfections for butt welding joint based on background subtraction, local thresholding and gray level approaches without any prior knowledge of the joint shapes. The background subtr...

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Main Author: Mohd Shah, Hairol Nizam
Format: Thesis
Language:English
English
Published: 2018
Subjects:
T Technology (General)
TS Manufactures
Online Access:http://eprints.utem.edu.my/id/eprint/22417/1/Local%20Threshold%20Identification%20And%20Gray%20Level%20Classification%20Of%20Butt%20Joint%20Welding%20Imperfections%20Using%20Robot%20Vision%20System.pdf
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institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Sulaiman, Marizan
topic T Technology (General)
TS Manufactures
spellingShingle T Technology (General)
TS Manufactures
Mohd Shah, Hairol Nizam
Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
description This research is carried out be able to automatically identify the joint position and classify the quality level of imperfections for butt welding joint based on background subtraction, local thresholding and gray level approaches without any prior knowledge of the joint shapes. The background subtraction and local thresholding approaches consist of image pre-processing, noise reduction and butt welding representation algorithms. The approaches can automatically recognize and locate the butt joint position of the starting, middle, auxiliary and ending point according to the three different joint shapes; straight line, tooth saw and curved joint shapes. The welding process was done by implemented an automatic coordinate conversion between camera (pixels) and KUKA welding robot coordinate (millimeters) from the KUKA welding robot and camera coordinate ratio. The ratio was determined by a camera and three reference point (origin, x-direction and y-direction) taken around workpiece. Hence, the quality level of imperfection for butt welding joint was classified using Gaussian Mix Model (GMM), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) classifiers according to their class of imperfection categories; good welds, excess welds, insufficient welds and no weld in each welding joint shape. These classifiers introduced 72 characteristics of feature values of gray pixels taken from co-occurrence matrix. The feature values consist of energy, correlation, homogeneity and contrast combine with gray absolute histogram of edge amplitude including additional characteristic features with scaled image factor by 0.5. The proposed approaches were validated through experiments with a KUKA welding robot in a realistic workshop environment. The results show that the approaches introduced in this research can detect, identify, recognize, locate the welding position and classify the quality level of imperfections for butt welding joint automatically without any prior knowledge of the joint shapes.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Mohd Shah, Hairol Nizam
author_facet Mohd Shah, Hairol Nizam
author_sort Mohd Shah, Hairol Nizam
title Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
title_short Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
title_full Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
title_fullStr Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
title_full_unstemmed Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
title_sort local threshold identification and gray level classification of butt joint welding imperfections using robot vision system
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty Of Electrical Engineering
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/22417/1/Local%20Threshold%20Identification%20And%20Gray%20Level%20Classification%20Of%20Butt%20Joint%20Welding%20Imperfections%20Using%20Robot%20Vision%20System.pdf
http://eprints.utem.edu.my/id/eprint/22417/2/Local%20threshold%20identification%20and%20gray%20level%20classification%20of%20butt%20joint%20welding%20imperfections%20using%20robot%20vision%20system.pdf
_version_ 1747834021097766912
spelling my-utem-ep.224172022-06-03T16:47:20Z Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system 2018 Mohd Shah, Hairol Nizam T Technology (General) TS Manufactures This research is carried out be able to automatically identify the joint position and classify the quality level of imperfections for butt welding joint based on background subtraction, local thresholding and gray level approaches without any prior knowledge of the joint shapes. The background subtraction and local thresholding approaches consist of image pre-processing, noise reduction and butt welding representation algorithms. The approaches can automatically recognize and locate the butt joint position of the starting, middle, auxiliary and ending point according to the three different joint shapes; straight line, tooth saw and curved joint shapes. The welding process was done by implemented an automatic coordinate conversion between camera (pixels) and KUKA welding robot coordinate (millimeters) from the KUKA welding robot and camera coordinate ratio. The ratio was determined by a camera and three reference point (origin, x-direction and y-direction) taken around workpiece. Hence, the quality level of imperfection for butt welding joint was classified using Gaussian Mix Model (GMM), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) classifiers according to their class of imperfection categories; good welds, excess welds, insufficient welds and no weld in each welding joint shape. These classifiers introduced 72 characteristics of feature values of gray pixels taken from co-occurrence matrix. The feature values consist of energy, correlation, homogeneity and contrast combine with gray absolute histogram of edge amplitude including additional characteristic features with scaled image factor by 0.5. The proposed approaches were validated through experiments with a KUKA welding robot in a realistic workshop environment. The results show that the approaches introduced in this research can detect, identify, recognize, locate the welding position and classify the quality level of imperfections for butt welding joint automatically without any prior knowledge of the joint shapes. 2018 Thesis http://eprints.utem.edu.my/id/eprint/22417/ http://eprints.utem.edu.my/id/eprint/22417/1/Local%20Threshold%20Identification%20And%20Gray%20Level%20Classification%20Of%20Butt%20Joint%20Welding%20Imperfections%20Using%20Robot%20Vision%20System.pdf text en public http://eprints.utem.edu.my/id/eprint/22417/2/Local%20threshold%20identification%20and%20gray%20level%20classification%20of%20butt%20joint%20welding%20imperfections%20using%20robot%20vision%20system.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=111332 phd doctoral Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Sulaiman, Marizan 1. Akkas, N., Karayel , D., Ozkan, S., S., Ogur, A. and Topal, B., 2013. 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