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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/11/1858 |
| _version_ | 1797509307754872832 |
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| author | Jeyaganesh Devaraj Aiman Ziout Jaber E. Abu Qudeiri |
| author_facet | Jeyaganesh Devaraj Aiman Ziout Jaber E. Abu Qudeiri |
| author_sort | Jeyaganesh Devaraj |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:16:50Z |
| format | Article |
| id | doaj.art-7833fc23bcf2487bae79c292598a2881 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T05:16:50Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-7833fc23bcf2487bae79c292598a28812023-11-23T00:24:40ZengMDPI AGMetals2075-47012021-11-011111185810.3390/met11111858Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process PerformanceJeyaganesh Devaraj0Aiman Ziout1Jaber E. Abu Qudeiri2Mechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, United Arab EmiratesMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, United Arab EmiratesMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, United Arab EmiratesThe 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.https://www.mdpi.com/2075-4701/11/11/1858dissimilar metal weldinggas metal arc weldinggrey-based Taguchi optimizationartificial neural network (ANN)adaptive neuro-fuzzy inference system (ANFIS) |
| spellingShingle | 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 Metals dissimilar metal welding gas metal arc welding grey-based Taguchi optimization artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) |
| title | 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_short | 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 |
| topic | dissimilar metal welding gas metal arc welding grey-based Taguchi optimization artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) |
| url | https://www.mdpi.com/2075-4701/11/11/1858 |
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