The Investigation of Monitoring Systems for SMAW Processes

The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitori...

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Autores principales:	Ahmed Samir Hamza, Osamah F. Abdulateef, Nabeel K. Abid Al-Sahib
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2009
Materias:	Artificial neural network monitoring fusion arc weld. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/c80ce91ce64a4e90890ce7a28f82920f
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spelling	oai:doaj.org-article:c80ce91ce64a4e90890ce7a28f82920f2021-12-02T01:10:05ZThe Investigation of Monitoring Systems for SMAW Processes1818-1171https://doaj.org/article/c80ce91ce64a4e90890ce7a28f82920f2009-01-01T00:00:00Zhttp://www.iasj.net/iasj?func=fulltext&aId=2360https://doaj.org/toc/1818-1171The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non-destructive (Hardness on HAZ) tests to investigate the quality control on the weld specimens. The experimental results obtained are then processed through the ANN model to control the welding process and predict the level of quality for different welding conditions. It has been deduced that the welding conditions (current, voltage, and travel speed) have a dominant factors that affect the weld quality and strength. Also we found that for certain welding condition, there was an optimum weld travel speed to obtain an optimum weld quality. The system supports quality control procedures and welding productivity without doing more periodic destructive mechanical test to dozens of samples.Ahmed Samir HamzaOsamah F. AbdulateefNabeel K. Abid Al-SahibAl-Khwarizmi College of Engineering – University of BaghdadarticleArtificial neural networkmonitoringfusion arc weld.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 5, Iss 3, Pp 1-15 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial neural network monitoring fusion arc weld. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Artificial neural network monitoring fusion arc weld. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Ahmed Samir Hamza Osamah F. Abdulateef Nabeel K. Abid Al-Sahib The Investigation of Monitoring Systems for SMAW Processes
description	The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non-destructive (Hardness on HAZ) tests to investigate the quality control on the weld specimens. The experimental results obtained are then processed through the ANN model to control the welding process and predict the level of quality for different welding conditions. It has been deduced that the welding conditions (current, voltage, and travel speed) have a dominant factors that affect the weld quality and strength. Also we found that for certain welding condition, there was an optimum weld travel speed to obtain an optimum weld quality. The system supports quality control procedures and welding productivity without doing more periodic destructive mechanical test to dozens of samples.
format	article
author	Ahmed Samir Hamza Osamah F. Abdulateef Nabeel K. Abid Al-Sahib
author_facet	Ahmed Samir Hamza Osamah F. Abdulateef Nabeel K. Abid Al-Sahib
author_sort	Ahmed Samir Hamza
title	The Investigation of Monitoring Systems for SMAW Processes
title_short	The Investigation of Monitoring Systems for SMAW Processes
title_full	The Investigation of Monitoring Systems for SMAW Processes
title_fullStr	The Investigation of Monitoring Systems for SMAW Processes
title_full_unstemmed	The Investigation of Monitoring Systems for SMAW Processes
title_sort	investigation of monitoring systems for smaw processes
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2009
url	https://doaj.org/article/c80ce91ce64a4e90890ce7a28f82920f
work_keys_str_mv	AT ahmedsamirhamza theinvestigationofmonitoringsystemsforsmawprocesses AT osamahfabdulateef theinvestigationofmonitoringsystemsforsmawprocesses AT nabeelkabidalsahib theinvestigationofmonitoringsystemsforsmawprocesses AT ahmedsamirhamza investigationofmonitoringsystemsforsmawprocesses AT osamahfabdulateef investigationofmonitoringsystemsforsmawprocesses AT nabeelkabidalsahib investigationofmonitoringsystemsforsmawprocesses
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The Investigation of Monitoring Systems for SMAW Processes

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