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Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network

In the process of MIG welding, the welding parameters such as welding current, arc voltage and welding speed has a significant effect to the weld joints mechanical properties and thus affect the quality of the weld from the aspect of mechanical properties. Even small variation in any of the cited pa...

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Main Author: Krishnamoorthy, Shasidaran
Format: Final Year Project
Language:English
Published: IRC 2016
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://utpedia.utp.edu.my/17273/1/FYP%20Shasidaran%20%2818625%29dissertation%20reportdocx.pdf
http://utpedia.utp.edu.my/17273/
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spelling my-utp-utpedia.172732017-03-02T14:11:28Z http://utpedia.utp.edu.my/17273/ Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network Krishnamoorthy, Shasidaran TJ Mechanical engineering and machinery In the process of MIG welding, the welding parameters such as welding current, arc voltage and welding speed has a significant effect to the weld joints mechanical properties and thus affect the quality of the weld from the aspect of mechanical properties. Even small variation in any of the cited parameters may have an important effect on depth of penetration. In this study, stainless steel 316L (316L) were chosen as the base metal to be tested using the main parameters of MIG welding. All the welding procedures were done according to the standards provided by American Welding Society (AWS). Physical properties preferred in any welded components are like tensile strength, yield strength and elongation. To achieve these physical properties, penetration is the key parameter to be verified. The process of mechanical properties testing was done accordance to ASTM E8/E8M standard, to make sure all the methods carried out are valid. Moreover, the welding process was performed using sets of input parameters to obtain specific results which was used to tabulate through mathematical modelling, as a procedure in optimizing the weld parameters, which is the regression model and the data sets were used to train and develop artificial neural network (ANN). In this project, a study on the welding parameters for pipeline was done by application of MIG welding by taking into account welding speed and wire feed rate as the parameters. The parameters are important to determine the tensile strength and weld bead penetration of the welded specimen. The data sets are necessary to train the Neural Network using Matlab ANN tool and hence enable to predict the desired output which was compared to the experimental data to check for validation. Therefore, the ANN predicted results shows a regression value of 0.95664 and 0.90948 for tensile strength and weld bead penetration respectively which means that the predicted value is near to the experimental value for the desired inputs which satisfy the objective of the study. IRC 2016-05 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/17273/1/FYP%20Shasidaran%20%2818625%29dissertation%20reportdocx.pdf Krishnamoorthy, Shasidaran (2016) Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Krishnamoorthy, Shasidaran
Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
description In the process of MIG welding, the welding parameters such as welding current, arc voltage and welding speed has a significant effect to the weld joints mechanical properties and thus affect the quality of the weld from the aspect of mechanical properties. Even small variation in any of the cited parameters may have an important effect on depth of penetration. In this study, stainless steel 316L (316L) were chosen as the base metal to be tested using the main parameters of MIG welding. All the welding procedures were done according to the standards provided by American Welding Society (AWS). Physical properties preferred in any welded components are like tensile strength, yield strength and elongation. To achieve these physical properties, penetration is the key parameter to be verified. The process of mechanical properties testing was done accordance to ASTM E8/E8M standard, to make sure all the methods carried out are valid. Moreover, the welding process was performed using sets of input parameters to obtain specific results which was used to tabulate through mathematical modelling, as a procedure in optimizing the weld parameters, which is the regression model and the data sets were used to train and develop artificial neural network (ANN). In this project, a study on the welding parameters for pipeline was done by application of MIG welding by taking into account welding speed and wire feed rate as the parameters. The parameters are important to determine the tensile strength and weld bead penetration of the welded specimen. The data sets are necessary to train the Neural Network using Matlab ANN tool and hence enable to predict the desired output which was compared to the experimental data to check for validation. Therefore, the ANN predicted results shows a regression value of 0.95664 and 0.90948 for tensile strength and weld bead penetration respectively which means that the predicted value is near to the experimental value for the desired inputs which satisfy the objective of the study.
format Final Year Project
author Krishnamoorthy, Shasidaran
author_facet Krishnamoorthy, Shasidaran
author_sort Krishnamoorthy, Shasidaran
title Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
title_short Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
title_full Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
title_fullStr Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
title_full_unstemmed Optimum Welding Parameters for Pipeline Welding Using Artificial Neural Network
title_sort optimum welding parameters for pipeline welding using artificial neural network
publisher IRC
publishDate 2016
url http://utpedia.utp.edu.my/17273/1/FYP%20Shasidaran%20%2818625%29dissertation%20reportdocx.pdf
http://utpedia.utp.edu.my/17273/
_version_ 1739832364476923904
score 13.211869

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