Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of...

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Autores principales:	Jeyaganesh Devaraj, Aiman Ziout, Jaber E. Abu Qudeiri
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dissimilar metal welding gas metal arc welding grey-based Taguchi optimization artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) Mining engineering. Metallurgy TN1-997
Acceso en línea:	https://doaj.org/article/7833fc23bcf2487bae79c292598a2881
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id	oai:doaj.org-article:7833fc23bcf2487bae79c292598a2881
record_format	dspace
spelling	oai:doaj.org-article:7833fc23bcf2487bae79c292598a28812021-11-25T18:22:28ZGrey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance10.3390/met111118582075-4701https://doaj.org/article/7833fc23bcf2487bae79c292598a28812021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4701/11/11/1858https://doaj.org/toc/2075-4701The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.Jeyaganesh DevarajAiman ZioutJaber E. Abu QudeiriMDPI AGarticledissimilar metal weldinggas metal arc weldinggrey-based Taguchi optimizationartificial neural network (ANN)adaptive neuro-fuzzy inference system (ANFIS)Mining engineering. MetallurgyTN1-997ENMetals, Vol 11, Iss 1858, p 1858 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dissimilar metal welding gas metal arc welding grey-based Taguchi optimization artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) Mining engineering. Metallurgy TN1-997
spellingShingle	dissimilar metal welding gas metal arc welding grey-based Taguchi optimization artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) Mining engineering. Metallurgy TN1-997 Jeyaganesh Devaraj Aiman Ziout Jaber E. Abu Qudeiri Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
description	The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.
format	article
author	Jeyaganesh Devaraj Aiman Ziout Jaber E. Abu Qudeiri
author_facet	Jeyaganesh Devaraj Aiman Ziout Jaber E. Abu Qudeiri
author_sort	Jeyaganesh Devaraj
title	Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
title_short	Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
title_full	Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
title_fullStr	Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
title_full_unstemmed	Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance
title_sort	grey-based taguchi multiobjective optimization and artificial intelligence-based prediction of dissimilar gas metal arc welding process performance
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7833fc23bcf2487bae79c292598a2881
work_keys_str_mv	AT jeyaganeshdevaraj greybasedtaguchimultiobjectiveoptimizationandartificialintelligencebasedpredictionofdissimilargasmetalarcweldingprocessperformance AT aimanziout greybasedtaguchimultiobjectiveoptimizationandartificialintelligencebasedpredictionofdissimilargasmetalarcweldingprocessperformance AT jabereabuqudeiri greybasedtaguchimultiobjectiveoptimizationandartificialintelligencebasedpredictionofdissimilargasmetalarcweldingprocessperformance
_version_	1718411266912944128

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

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