Monitoring and Quality Control of Stud Welding

This study is conducted to carry out a straightforward way appropriate for quality monitoring and stability of arc stud welding process, followed by a number of procedures to control the quality of welded samples, namely torque destructive testing and visual inspection context. Those procedures we...

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Autores principales:	Nabeel K. Abid Al-Sahib, Hussam K. Abdul Ameer, Saif Ghazy Faisal Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2009
Materias:	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/66b98cdfb50045d08a65908f7f64ba4c
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spelling	oai:doaj.org-article:66b98cdfb50045d08a65908f7f64ba4c2021-12-02T06:35:45ZMonitoring and Quality Control of Stud Welding1818-11712312-0789https://doaj.org/article/66b98cdfb50045d08a65908f7f64ba4c2009-03-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/520https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 This study is conducted to carry out a straightforward way appropriate for quality monitoring and stability of arc stud welding process, followed by a number of procedures to control the quality of welded samples, namely torque destructive testing and visual inspection context. Those procedures were being performed to support the monitoring system and verify its validity. Thus, continuous on-line monitoring guarantees earlier discovering stud welding defects and avoiding weld repeatability. On-line welding electronic monitoring system is for non destructive determining if a just completed weld is satisfactory or unsatisfactory, depending on welding current peak value detected by the system. Also, it has been observed significant harmonize which is mutually linking the monitored current peak values and quality control measures. So this concept is accordingly contributed in the process of supporting the fundamental objective of this research. On the other hand, two feed-forward neural networks have been developed for monitoring and control arc stud welding quality. First network predicts two output quality parameters (current peak value) and (torque testing value at failure). Second, predicts one output quality parameter (visual inspection). Networks have been trained to a set of data, which made them ready to receive new information for subsequent quality parameters prediction. Both networks showed up good response and acceptable results. Nabeel K. Abid Al-SahibHussam K. Abdul AmeerSaif Ghazy Faisal IbrahimAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 5, Iss 1 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Nabeel K. Abid Al-Sahib Hussam K. Abdul Ameer Saif Ghazy Faisal Ibrahim Monitoring and Quality Control of Stud Welding
description	This study is conducted to carry out a straightforward way appropriate for quality monitoring and stability of arc stud welding process, followed by a number of procedures to control the quality of welded samples, namely torque destructive testing and visual inspection context. Those procedures were being performed to support the monitoring system and verify its validity. Thus, continuous on-line monitoring guarantees earlier discovering stud welding defects and avoiding weld repeatability. On-line welding electronic monitoring system is for non destructive determining if a just completed weld is satisfactory or unsatisfactory, depending on welding current peak value detected by the system. Also, it has been observed significant harmonize which is mutually linking the monitored current peak values and quality control measures. So this concept is accordingly contributed in the process of supporting the fundamental objective of this research. On the other hand, two feed-forward neural networks have been developed for monitoring and control arc stud welding quality. First network predicts two output quality parameters (current peak value) and (torque testing value at failure). Second, predicts one output quality parameter (visual inspection). Networks have been trained to a set of data, which made them ready to receive new information for subsequent quality parameters prediction. Both networks showed up good response and acceptable results.
format	article
author	Nabeel K. Abid Al-Sahib Hussam K. Abdul Ameer Saif Ghazy Faisal Ibrahim
author_facet	Nabeel K. Abid Al-Sahib Hussam K. Abdul Ameer Saif Ghazy Faisal Ibrahim
author_sort	Nabeel K. Abid Al-Sahib
title	Monitoring and Quality Control of Stud Welding
title_short	Monitoring and Quality Control of Stud Welding
title_full	Monitoring and Quality Control of Stud Welding
title_fullStr	Monitoring and Quality Control of Stud Welding
title_full_unstemmed	Monitoring and Quality Control of Stud Welding
title_sort	monitoring and quality control of stud welding
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2009
url	https://doaj.org/article/66b98cdfb50045d08a65908f7f64ba4c
work_keys_str_mv	AT nabeelkabidalsahib monitoringandqualitycontrolofstudwelding AT hussamkabdulameer monitoringandqualitycontrolofstudwelding AT saifghazyfaisalibrahim monitoringandqualitycontrolofstudwelding
_version_	1718399817133064192

Monitoring and Quality Control of Stud Welding

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