Print Email Facebook Twitter High-Throughput Quality Inspection of Solar Cells Using Deep Learning Under Consideration of Its Sustainability Impact Title High-Throughput Quality Inspection of Solar Cells Using Deep Learning Under Consideration of Its Sustainability Impact Author Reinhard, Marko (TU Delft Technology, Policy and Management) Contributor Santbergen, R. (mentor) Blanco, Carlos Felipe (mentor) Degree granting institution Delft University of TechnologyUniversiteit Leiden Programme Industrial Ecology Date 2022-08-31 Abstract To meet global market demands, it will remain important to further scale up photovoltaics (PV) production. During the production of solar cells, several defects can occur. Current approaches in quality inspection are reaching their speed limits. This thesis project evaluates the feasibility of faster quality inspection by using deep learning-based computer vision (CV) algorithms to detect production defects without human supervision at high speeds. The goal is to achieve this while reducing the necessary manual efforts to label (annotate) defects in the training data of such algorithms.The second goal of the project is to investigate in which ways and to which extent this innovation can impact the sustainability performance of the solar cell production process. Multiple scenarios are investigated using a Life Cycle Assessment (LCA) model. The results are used to estimate the potential large-scale impact of increasing solar cell production throughput. Subject PhotovoltaicsDeep LearningComputer VisionSustainabilityElectroluminescencequality InspectionEnvironmental impact To reference this document use: http://resolver.tudelft.nl/uuid:e30c14c6-46f9-4941-b992-879d62ffa542 Embargo date 2023-10-01 Part of collection Student theses Document type master thesis Rights © 2022 Marko Reinhard Files PDF Thesis_Marko_Reinhard_Hig ... Impact.pdf 11.6 MB Close viewer /islandora/object/uuid:e30c14c6-46f9-4941-b992-879d62ffa542/datastream/OBJ/view