含干扰区域的航空铣削件导孔视觉测量方法

高光亚; 闫娟; 杨慧斌; 刘亚彪

doi:10.12299/jsues.23-0173

含干扰区域的航空铣削件导孔视觉测量方法

doi: 10.12299/jsues.23-0173

上海工程技术大学机械与汽车工程学院, 上海 201620

详细信息

作者简介:
高光亚（1998−），男，硕士生，研究方向为机器视觉。E-mail：1870955589@qq.com

通讯作者:
闫　娟（1979−），女，副教授，硕士，研究方向为智能控制。E-mail：aliceyan_shu@163.com

中图分类号: TP183；TH17
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- 文章访问数: 127
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- PDF下载量: 49
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-03
- 刊出日期: 2024-06-30

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

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

摘要

摘要: 在航空铣削零件测量过程中，传统图像处理方法无法有效测量含有切屑和切削液等干扰因素的铣削零件导孔尺寸。针对该问题，提出MPTransUNet模型检测工件图像的干扰区域；采用图像纹理修复方法对检测图像进行修复，并设计了多尺度边缘检测算法提取导孔边缘像素；结合随机抽样一致算法和最小二乘法，对边缘像素点筛选和拟合得到导孔几何尺寸。最后，以航空装夹板件为例验证了该方法的有效性，导孔孔径的测量精度为0.03 mm，可以满足航空铣削零件质检要求。
- 深度学习 /
- 边缘检测 /
- 图像分割 /
- 视觉测量
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

HTML全文

图 1 某框类零件上导孔示意图

Figure 1. Schematic diagram of guide holes on workpieces

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图 2 导孔视觉检测流程

Figure 2. Visual inspection process of guide hole

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图 3 MPTransUNet网络模型

Figure 3. MPTransUNet network model

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图 4 导孔图像数据集

Figure 4. Guide hole image dataset

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图 5 在线视觉测量系统的硬件平台

Figure 5. Hardware platform of online vision measurement system

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图 6 试验工件

Figure 6. Experimental workpieces

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图 7 试验图像处理结果

Figure 7. Experimental image processing results

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图 8 孔径重复性测量结果

Figure 8. Repeatability measurement results of holes

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图 9 孔径重复测量误差可视化

Figure 9. Visualization of holes repetitive measurement error

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图 10 尺寸偏差结果

Figure 10. Bias test results

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表 1 单因素t检验偏差结果

Table 1. Single sample t-test results of biases

序号样本量平均偏差/mm t统计量 P-value

1 10 0.0229 −1.1307 0.2874
2 10 0.0224 −1.1099 0.2958
3 10 0.0274 −1.3618 0.2064

下载: 导出CSV

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