Quality monitoring of hybrid welding processes: A comprehensive review
Hybrid welding processes have gained significant attention due to their high efficiency and exceptional welding properties. However, there are still significant technological challenges in achieving consistent quality and suppressing welding defects. To overcome this challenge, researchers have focu...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2024-12-01
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| Series: | Archives of Control Sciences |
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| Online Access: | https://journals.pan.pl/Content/133809/art08.pdf |
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| _version_ | 1850101471057543168 |
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| author | Solomon Habtamu Tessema Dariusz Bismor |
| author_facet | Solomon Habtamu Tessema Dariusz Bismor |
| author_sort | Solomon Habtamu Tessema |
| collection | DOAJ |
| description | Hybrid welding processes have gained significant attention due to their high efficiency and exceptional welding properties. However, there are still significant technological challenges in achieving consistent quality and suppressing welding defects. To overcome this challenge, researchers have focused on the integration of visual analysis techniques, numerical simulation techniques, and advanced technologies such as artificial intelligence/machine learning (AI/ML) and digital twins. This comprehensive review synthesizes current knowledge on quality monitoring in hybrid welding, encompassing an overview of hybrid welding processes, quality assurance, monitoring techniques, key performance indicators, and advancements in monitoring techniques. Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. Notably, the review emphasizes the incorporation of artificial intelligence (AI) and digital twin technologies into quality monitoring frameworks. Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. The findings underscore the crucial role of sensor technology, AI/ML, and digital twin integration in enhancing defect detection accuracy, improving welded joint quality, and control in hybrid welding. In addition to improving the quality of welded joints, this integration paves the way for further developments in welding technology. |
| format | Article |
| id | doaj-art-010f999b65fd4572a8b2eb20f2bae721 |
| institution | DOAJ |
| issn | 1230-2384 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Control Sciences |
| spelling | doaj-art-010f999b65fd4572a8b2eb20f2bae7212025-08-20T02:40:00ZengPolish Academy of SciencesArchives of Control Sciences1230-23842024-12-01vol. 34No 4https://doi.org/10.24425/acs.2024.153104Quality monitoring of hybrid welding processes: A comprehensive reviewSolomon Habtamu Tessema0https://orcid.org/0009-0008-9562-417XDariusz Bismor1https://orcid.org/0000-0003-4758-3592Faculty of Automatic Control, Electronics and Computer Science, Department of Measurements and Control Systems, Silesian University of Technology, 44-100 Gliwice, PolandFaculty of Automatic Control, Electronics and Computer Science, Department of Measurements and Control Systems, Silesian University of Technology, 44-100 Gliwice, PolandHybrid welding processes have gained significant attention due to their high efficiency and exceptional welding properties. However, there are still significant technological challenges in achieving consistent quality and suppressing welding defects. To overcome this challenge, researchers have focused on the integration of visual analysis techniques, numerical simulation techniques, and advanced technologies such as artificial intelligence/machine learning (AI/ML) and digital twins. This comprehensive review synthesizes current knowledge on quality monitoring in hybrid welding, encompassing an overview of hybrid welding processes, quality assurance, monitoring techniques, key performance indicators, and advancements in monitoring techniques. Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. Notably, the review emphasizes the incorporation of artificial intelligence (AI) and digital twin technologies into quality monitoring frameworks. Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. The findings underscore the crucial role of sensor technology, AI/ML, and digital twin integration in enhancing defect detection accuracy, improving welded joint quality, and control in hybrid welding. In addition to improving the quality of welded joints, this integration paves the way for further developments in welding technology.https://journals.pan.pl/Content/133809/art08.pdfhybrid weldingdefect detectionquality monitoringartificial intelligencedigital twins |
| spellingShingle | Solomon Habtamu Tessema Dariusz Bismor Quality monitoring of hybrid welding processes: A comprehensive review Archives of Control Sciences hybrid welding defect detection quality monitoring artificial intelligence digital twins |
| title | Quality monitoring of hybrid welding processes: A comprehensive review |
| title_full | Quality monitoring of hybrid welding processes: A comprehensive review |
| title_fullStr | Quality monitoring of hybrid welding processes: A comprehensive review |
| title_full_unstemmed | Quality monitoring of hybrid welding processes: A comprehensive review |
| title_short | Quality monitoring of hybrid welding processes: A comprehensive review |
| title_sort | quality monitoring of hybrid welding processes a comprehensive review |
| topic | hybrid welding defect detection quality monitoring artificial intelligence digital twins |
| url | https://journals.pan.pl/Content/133809/art08.pdf |
| work_keys_str_mv | AT solomonhabtamutessema qualitymonitoringofhybridweldingprocessesacomprehensivereview AT dariuszbismor qualitymonitoringofhybridweldingprocessesacomprehensivereview |