Automated visual detection of external welding defect using embedded machine learning

This project is designed to perform a quality check on a welded material's surface. Welding is the most useful process for joining materials in the manufacturing, automotive, and construction industries. As a result, in order to meet a client requirements, all welding works must be inspected, w...

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Main Author:	Abdul Mutalib, Nurfariza Akmar
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99550/1/NurfarizaAkmarAbdulMSKE2022.pdf
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id	my-utm-ep.99550
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spelling	my-utm-ep.995502023-02-28T08:28:41Z Automated visual detection of external welding defect using embedded machine learning 2022 Abdul Mutalib, Nurfariza Akmar TK Electrical engineering. Electronics Nuclear engineering This project is designed to perform a quality check on a welded material's surface. Welding is the most useful process for joining materials in the manufacturing, automotive, and construction industries. As a result, in order to meet a client requirements, all welding works must be inspected, with the most basic method is nondestructive visual inspection testing. Surface inspection can be performed using nondestructive methods such as dye-penetration testing and magnetic particle inspection. However, those methods are expensive, take a long time to complete inspections, and require a complex procedure to operate. This project proposed an automated visual inspection system that relies on embedded machine learning. This system is made up of a camera, a microcontroller, and web server. The camera-based system will detect any defects on the welded material, which will then be processed by the microcontroller before being displayed on the web server through Wi-Fi connection. As a result, this system was split into two parts: software and hardware. The Arduino IDE was used to programme the system, and Edge Impulse was used to develop the embedding machine learning model. This system's hardware consists only ESP32- CAM module. As a result, it is possible to create an automated system that is user friendly and has simple operation procedure. Furthermore, a low-cost system with a short inspection time have been developed. A sharp and clear image with single type of defect appear on the workpiece help to provide best performance of defect classification. However, most likely same image captured, e.g. good welding and overlap defect, decrease the effectiveness of detection. The detection accuracy of this system can reach up to 95% with more training data provided, as for this project each defects detection accuracy are falls between 75 - 94 percent. In conclusion, the use of embedded machine learning in non-destructive testing is successful for the visual inspection method. 2022 Thesis http://eprints.utm.my/id/eprint/99550/ http://eprints.utm.my/id/eprint/99550/1/NurfarizaAkmarAbdulMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149728 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abdul Mutalib, Nurfariza Akmar Automated visual detection of external welding defect using embedded machine learning
description	This project is designed to perform a quality check on a welded material's surface. Welding is the most useful process for joining materials in the manufacturing, automotive, and construction industries. As a result, in order to meet a client requirements, all welding works must be inspected, with the most basic method is nondestructive visual inspection testing. Surface inspection can be performed using nondestructive methods such as dye-penetration testing and magnetic particle inspection. However, those methods are expensive, take a long time to complete inspections, and require a complex procedure to operate. This project proposed an automated visual inspection system that relies on embedded machine learning. This system is made up of a camera, a microcontroller, and web server. The camera-based system will detect any defects on the welded material, which will then be processed by the microcontroller before being displayed on the web server through Wi-Fi connection. As a result, this system was split into two parts: software and hardware. The Arduino IDE was used to programme the system, and Edge Impulse was used to develop the embedding machine learning model. This system's hardware consists only ESP32- CAM module. As a result, it is possible to create an automated system that is user friendly and has simple operation procedure. Furthermore, a low-cost system with a short inspection time have been developed. A sharp and clear image with single type of defect appear on the workpiece help to provide best performance of defect classification. However, most likely same image captured, e.g. good welding and overlap defect, decrease the effectiveness of detection. The detection accuracy of this system can reach up to 95% with more training data provided, as for this project each defects detection accuracy are falls between 75 - 94 percent. In conclusion, the use of embedded machine learning in non-destructive testing is successful for the visual inspection method.
format	Thesis
qualification_level	Master's degree
author	Abdul Mutalib, Nurfariza Akmar
author_facet	Abdul Mutalib, Nurfariza Akmar
author_sort	Abdul Mutalib, Nurfariza Akmar
title	Automated visual detection of external welding defect using embedded machine learning
title_short	Automated visual detection of external welding defect using embedded machine learning
title_full	Automated visual detection of external welding defect using embedded machine learning
title_fullStr	Automated visual detection of external welding defect using embedded machine learning
title_full_unstemmed	Automated visual detection of external welding defect using embedded machine learning
title_sort	automated visual detection of external welding defect using embedded machine learning
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99550/1/NurfarizaAkmarAbdulMSKE2022.pdf
_version_	1776100617483190272

Automated visual detection of external welding defect using embedded machine learning

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