Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding
In recent years, welding feedback control systems and weld quality estimation systems have been developed with the use of artificial intelligence to increase the quality consistency of robotic welding solutions. This paper introduces the utilization of an intelligent welding system (IWS) for feedbac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/7/3/102 |
| _version_ | 1797594012065988608 |
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| author | Sakari Penttilä Hannu Lund Tuomas Skriko |
| author_facet | Sakari Penttilä Hannu Lund Tuomas Skriko |
| author_sort | Sakari Penttilä |
| collection | DOAJ |
| description | In recent years, welding feedback control systems and weld quality estimation systems have been developed with the use of artificial intelligence to increase the quality consistency of robotic welding solutions. This paper introduces the utilization of an intelligent welding system (IWS) for feedback controlling the welding process. In this study, the GMAW process is controlled by a backpropagation neural network (NN). The feedback control of the welding process is controlled by the input parameters; root face and root gap, measured by a laser triangulation sensor. The NN is trained to adapt NN output parameters; wire feed and arc voltage override of the weld power source, in order to achieve consistent weld quality. The NN is trained offline with the specific parameter window in varying weld conditions, and the testing of the system is performed on separate specimens to evaluate the performance of the system. The butt-weld case is explained starting from the experimental setup to the training process of the IWS, optimization and operating principle. Furthermore, the method to create IWS for the welding process is explained. The results show that the developed IWS can adapt to the welding conditions of the seam and feedback control the welding process to achieve consistent weld quality outcomes. The method of using NN as a welding process parameter optimization tool was successful. The results of this paper indicate that an increased number of sensors could be applied to measure and control the welding process with the developed IWS. |
| first_indexed | 2024-03-11T02:17:35Z |
| format | Article |
| id | doaj.art-9d995ec9a7ef462597f0a09049103b83 |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-11T02:17:35Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-9d995ec9a7ef462597f0a09049103b832023-11-18T11:05:32ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942023-05-017310210.3390/jmmp7030102Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc WeldingSakari Penttilä0Hannu Lund1Tuomas Skriko2Department of Mechanical Engineering, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, FinlandDepartment of Mechanical Engineering, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, FinlandDepartment of Mechanical Engineering, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, FinlandIn recent years, welding feedback control systems and weld quality estimation systems have been developed with the use of artificial intelligence to increase the quality consistency of robotic welding solutions. This paper introduces the utilization of an intelligent welding system (IWS) for feedback controlling the welding process. In this study, the GMAW process is controlled by a backpropagation neural network (NN). The feedback control of the welding process is controlled by the input parameters; root face and root gap, measured by a laser triangulation sensor. The NN is trained to adapt NN output parameters; wire feed and arc voltage override of the weld power source, in order to achieve consistent weld quality. The NN is trained offline with the specific parameter window in varying weld conditions, and the testing of the system is performed on separate specimens to evaluate the performance of the system. The butt-weld case is explained starting from the experimental setup to the training process of the IWS, optimization and operating principle. Furthermore, the method to create IWS for the welding process is explained. The results show that the developed IWS can adapt to the welding conditions of the seam and feedback control the welding process to achieve consistent weld quality outcomes. The method of using NN as a welding process parameter optimization tool was successful. The results of this paper indicate that an increased number of sensors could be applied to measure and control the welding process with the developed IWS.https://www.mdpi.com/2504-4494/7/3/102artificial intelligenceneural networkLevenberg–Marquardt Algorithmfeedback controllaser triangulationGMAW |
| spellingShingle | Sakari Penttilä Hannu Lund Tuomas Skriko Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding Journal of Manufacturing and Materials Processing artificial intelligence neural network Levenberg–Marquardt Algorithm feedback control laser triangulation GMAW |
| title | Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding |
| title_full | Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding |
| title_fullStr | Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding |
| title_full_unstemmed | Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding |
| title_short | Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding |
| title_sort | possibilities of artificial intelligence enabled feedback control system in robotized gas metal arc welding |
| topic | artificial intelligence neural network Levenberg–Marquardt Algorithm feedback control laser triangulation GMAW |
| url | https://www.mdpi.com/2504-4494/7/3/102 |
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