摘要
生产现场中,尤其是小批量大规模设备的装配和调试过程中,螺钉需求量大,螺钉连接状态对装配质量有着重大影响,目前对于该过程的检验仍需要工人目视检验并手动记录,这一过程主观性强,自动化水平低,效率低下;针对装配过程中的螺钉状态进行检测,基于视觉检测的方式,识别螺钉的拧紧程度,提高检测准确率和效率;首先,通过对线性特征的有效匹配来对螺钉进行定位,之后,使用全局阈值和自适应阈值结合的方法对螺钉区域进行有效分割,提取螺钉区域的特征,最后对提取的拧紧螺钉和未拧紧螺钉的特征进行分类识别,识别出设备上的未拧紧螺钉,检测准确率达到93%以上,检测精度可达到2mm以内;采用便携式检测设备,方便部署,可广泛适用于小批量大规模设备的多个应用场景的装配质量检测中。
In the production site,especially in the assembly and debugging process of small batch and large-scale equipment,there is a large demand for screws,and the screw connection status has a significant impact on the assembly quality.At present,the inspection of this process still needs workers’visual inspection and manual record,which is subjective,low automation level and low efficiency.According to the screw state detection in the assembly process,based on the way of visual detection,the screw tightening degree is identified to improve the detection accuracy and efficiency.First of all,the screw is positioned by effective matching of linear features.Then,the screw area is effectively segmented to extract the features of screw area by using a combination of global and adaptive thresholds.Finally,the extracted features of tightened screws and untightened screws are classified and identified to identify the untightened screws on the equipment,with the detection accuracy over 93%and measurement accuracy of less than 2 mm.Moreover,the portable detection equipment is adopted,which is convenient for deployment and can be used in the assembly quality inspection of small batch and large-scale equipment in multiple application mode.
作者
姜金岭
孙洁
白伟光
Jiang Jinling;Sun Jie;Bai Weiguang(Beijing Aerospace Measurement and Control Technology Corporation,Beijing 100041;Beijing Key Laboratory of High-speed Transport Intelligent Diagnostic and Health Management,Beijing 100041,China;National and Local Joint Engineering Research Center of Equipment Life Cycle Condition Monitoring and Health Management Technology and Application,Beijing 100041,China)
出处
《计算机测量与控制》
2020年第1期130-134,共5页
Computer Measurement &Control
基金
基础科研项目(JCKY2017204C023)
关键词
线特征检测
特征匹配
仿射变换
图像分割
Line feature detection
feature matching
affine transformation
image segmentation