Transactions of Nanjing University of Aeronautics & Astronautics
Method for Aircraft Sheet Metal Part Recognition Based on Cascading Virtual-Real Fusion
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Abstract:
A cascading virtual-real fusion approach is proposed to recognize various aircraft sheet metal parts （SMPs） with remarkable similarities. The SMPs are identified and numbered by cascading “Rough” “Fine”， and “Preferred” Through the virtual-real fusion approach of virtual workbench modelling and physical workbench actual recognition. The “Rough” SMP set is identified by gathering the main direction image of the sheet metal item on the real workbench and obtaining an eight-dimensional （8D） shape-description vector from the image. This leads to the discovery of a candidate SMP set. Then， template matching is conducted on the candidate SMP set based on the image’s grey information， and “Fine” matching is obtained. A quantitative index of recognition reliability is proposed to subsequently initiate the “Preferred” recognition process， which is accomplished with an augmented reality 3D projection. The effectiveness and superiority of the propsed method are verified by real experiments， and the best accuracy rate of 96.9% is achieved in testing parts. With the help of 3D projection， the accuracy of man-machine combination is 100%.
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This work was partly supported by Chengdu Aircraft Industrial （Group） Co. Ltd.， and the Natural Science Foundation of China （No.52075260）.
Clc Number:
TP29
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