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- W2897771462 abstract "Automated assembly technology is a major component of modern aerospace manufacturing. Hole-Making Robot (HMR)s can significantly improve the efficiency of the aircraft manufacturing and can also ensure reliability of processing. The machining accuracy of the HMR depends on the precision of the mechanism, the accuracy of hole-positioning, and the vertical degree of the hole. Evaluating the precision of the hole system requires a complete set of inspection systems to measure the relevant hole parameters. Visual detection technology can provide more information on the object, which in theory is better suited to simulate real world applications. Due to the rapid development of current visual technology and the actual demand of aviation holes, this paper attempts to apply Convolution Neural Network (CNN) technology to the concrete task of visual inspection and improve the performance of visual inspection systems. Hole classification and hole flaw detection are realized via two feed-forward layers. We created a dataset of 1300 images of holes for testing. These were collected from various sources. The highest accuracy was 98.15% and was achieved using the two feed-forward neural network algorithm." @default.
- W2897771462 created "2018-10-26" @default.
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- W2897771462 date "2018-09-01" @default.
- W2897771462 modified "2023-09-25" @default.
- W2897771462 title "Using two feed-forward layers for fault detection of hole" @default.
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- W2897771462 doi "https://doi.org/10.1088/1742-6596/1074/1/012143" @default.
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