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- W3135628566 abstract "The quality of uni-directional tape in its production process is affected by environmental conditions like temperature and production speed. In this paper, computer vision algorithms on the scanned images are needed to be used in this context to detect and classify tape damages during the manufacturing procedure. We perform a comparative study among famous feature descriptors for fault candidate generation, then propose own features for fault detection. We investigate various machine learning techniques to find best model for the classification problem. The empirical results demonstrate the high performance of the proposed system and show preference of random forest and canny edges for classifier and feature generator respectively.KeywordsUnidirectional thermoplastic compositesQuality controlFeature extractionMachine-learningObject detection" @default.
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- W3135628566 date "2021-01-01" @default.
- W3135628566 modified "2023-09-25" @default.
- W3135628566 title "Fault Detection in Uni-Directional Tape Production Using Image Processing" @default.
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- W3135628566 doi "https://doi.org/10.1007/978-3-030-68799-1_52" @default.
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