A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing
As renewable energy production grows, the photovoltaic (PV) module manufacturing process has received worldwide attention. In 2019, the total sales of PV modules were 1.7 billion U.S. dollars, and 78.7% of PV modules were made in South Korea. However, Korean manufacturers are facing high production...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/4/285 |
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| author | In-Bae Lee Youngjin Kim Sojung Kim |
| author_facet | In-Bae Lee Youngjin Kim Sojung Kim |
| author_sort | In-Bae Lee |
| collection | DOAJ |
| description | As renewable energy production grows, the photovoltaic (PV) module manufacturing process has received worldwide attention. In 2019, the total sales of PV modules were 1.7 billion U.S. dollars, and 78.7% of PV modules were made in South Korea. However, Korean manufacturers are facing high production costs due to high domestic labor costs and long-distance raw material procurement, making it difficult to produce price-competitive PV modules. In this situation, the best alternative for Korean manufacturers to gain a competitive edge is to produce high-quality PV modules. To this end, this study is going to propose a novel data-driven machine vision framework for the quality management of a PV manufacturing process consisting of seven stages, including tabbing, auto bussing, electro luminescence (EL), laminating, fame station, frame, and junction box. Particularly, the framework uses machine vision to analyze image data collected from an actual PV module manufacturing facility in South Korea. Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. This experiment shows that the proposed framework enables the detection of PV module defects in electroluminescence (EL) and tabbing operations with a fault detection accuracy of over 95%. Therefore, the proposed framework enables a reduction in the number of defects, and this helps to improve quality loss during the PV module manufacturing process. |
| format | Article |
| id | doaj-art-ed219aeff4e9480e9cccb91ec0d8877a |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Machines |
| spelling | doaj-art-ed219aeff4e9480e9cccb91ec0d8877a2025-08-20T02:18:09ZengMDPI AGMachines2075-17022025-03-0113428510.3390/machines13040285A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module ManufacturingIn-Bae Lee0Youngjin Kim1Sojung Kim2Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaAs renewable energy production grows, the photovoltaic (PV) module manufacturing process has received worldwide attention. In 2019, the total sales of PV modules were 1.7 billion U.S. dollars, and 78.7% of PV modules were made in South Korea. However, Korean manufacturers are facing high production costs due to high domestic labor costs and long-distance raw material procurement, making it difficult to produce price-competitive PV modules. In this situation, the best alternative for Korean manufacturers to gain a competitive edge is to produce high-quality PV modules. To this end, this study is going to propose a novel data-driven machine vision framework for the quality management of a PV manufacturing process consisting of seven stages, including tabbing, auto bussing, electro luminescence (EL), laminating, fame station, frame, and junction box. Particularly, the framework uses machine vision to analyze image data collected from an actual PV module manufacturing facility in South Korea. Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. This experiment shows that the proposed framework enables the detection of PV module defects in electroluminescence (EL) and tabbing operations with a fault detection accuracy of over 95%. Therefore, the proposed framework enables a reduction in the number of defects, and this helps to improve quality loss during the PV module manufacturing process.https://www.mdpi.com/2075-1702/13/4/285renewable energymachine visionartificial intelligencephotovoltaic module |
| spellingShingle | In-Bae Lee Youngjin Kim Sojung Kim A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing Machines renewable energy machine vision artificial intelligence photovoltaic module |
| title | A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing |
| title_full | A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing |
| title_fullStr | A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing |
| title_full_unstemmed | A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing |
| title_short | A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing |
| title_sort | data driven machine vision framework for quality management in photovoltaic module manufacturing |
| topic | renewable energy machine vision artificial intelligence photovoltaic module |
| url | https://www.mdpi.com/2075-1702/13/4/285 |
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