Visual measurement method for aircraft milling parts' guide hole with interference area

GAO Guangya; YAN Juan; YANG Huibin; LIU Yabiao

doi:10.12299/jsues.23-0173

Volume 38 Issue 2

Jun. 2024

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GAO Guangya, YAN Juan, YANG Huibin, LIU Yabiao. Visual measurement method for aircraft milling parts' guide hole with interference area[J]. Journal of Shanghai University of Engineering Science, 2024, 38(2): 111-117. doi: 10.12299/jsues.23-0173

Citation:

GAO Guangya, YAN Juan, YANG Huibin, LIU Yabiao. Visual measurement method for aircraft milling parts' guide hole with interference area[J]. Journal of Shanghai University of Engineering Science, 2024, 38(2): 111-117. doi: 10.12299/jsues.23-0173

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Visual measurement method for aircraft milling parts' guide hole with interference area

doi: 10.12299/jsues.23-0173

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2023-08-03
Publish Date: 2024-06-30

Abstract

Abstract

In the measurement process of aerospace milling parts, traditional image processing methods cannot effectively measure the size of guide holes in milling parts that contain interference factors such as chips and cutting fluid. To address this issue, a visual measurement method for workpiece aperture based on the TransUNet model was proposed to detect interference regions in the workpiece image, and a mixed pooling module was introduced to improve the model's feature recognition range for chips and cutting fluids. Then, an image direction texture repair method was used to repair the detection image, and the guide hole edge pixels were extracted by improving the edge detection algorithm. Combining the random sampling consensus algorithm and least squares method, the guide hole geometric size was obtained by filtering and fitting the edge pixel points. Finally, the effectiveness of the method was verified by an example of aerospace fixture plate. The measurement accuracy of the guide hole aperture is 0.03 mm, which can meets the requirements of quality inspection for aerospace milling parts.
- deep learning,
- edge detection,
- image segmentation,
- visual measurement

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References(19)

References

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