Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning
Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human wel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/10/4/451 |
| _version_ | 1797626405120376832 |
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| author | Martin A. Kesse Eric Buah Heikki Handroos Godwin K. Ayetor |
| author_facet | Martin A. Kesse Eric Buah Heikki Handroos Godwin K. Ayetor |
| author_sort | Martin A. Kesse |
| collection | DOAJ |
| description | Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool. |
| first_indexed | 2024-03-11T10:09:58Z |
| format | Article |
| id | doaj.art-841e4a03cfbd490082f9509b22d8a11a |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-11T10:09:58Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-841e4a03cfbd490082f9509b22d8a11a2023-11-16T14:34:57ZengMDPI AGMetals2075-47012020-03-0110445110.3390/met10040451Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep LearningMartin A. Kesse0Eric Buah1Heikki Handroos2Godwin K. Ayetor3School of Energy Systems, Mechanical Engineering Department, LUT University, 53850 Lappeenranta, FinlandSchool of Energy Systems, Sustainability Science Department, LUT University, 53850 Lappeenranta, FinlandSchool of Energy Systems, Mechanical Engineering Department, LUT University, 53850 Lappeenranta, FinlandDepartment of Mechanical Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi AK-039-5028, GhanaRecent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.https://www.mdpi.com/2075-4701/10/4/451TIG weldingartificial intelligencedeep neural networkautomation |
| spellingShingle | Martin A. Kesse Eric Buah Heikki Handroos Godwin K. Ayetor Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning Metals TIG welding artificial intelligence deep neural network automation |
| title | Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning |
| title_full | Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning |
| title_fullStr | Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning |
| title_full_unstemmed | Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning |
| title_short | Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning |
| title_sort | development of an artificial intelligence powered tig welding algorithm for the prediction of bead geometry for tig welding processes using hybrid deep learning |
| topic | TIG welding artificial intelligence deep neural network automation |
| url | https://www.mdpi.com/2075-4701/10/4/451 |
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