摘要
研究了一种对工业计算机断层(CT)图像与计算机辅助设计(CAD)模型进行比对检测,分析工件制造误差的方法。首先,用模板自适应细胞神经网络提取工业CT图像边缘,并进行三方向CT切片边缘数据融合处理以获得完整的三维边缘面。然后,先结合主成分分析和最小包围盒的思想对CT边缘面数据与工件的CAD模型实现粗配准,再用奇异值分解-迭代最近点算法对其进行精配准,其中最近点对的求取用k-d树进行加速,从而实现对工件制造误差的分析。实验结果显示,文中的方法能够实现工件的比对检测,自动化程度高、能直观显示误差分布且精度高,表明通过改进工件CT图像与CAD模型的比对检测方法,可将工业CT技术用于制造工艺分析与改进中。
A method to analyze the manufacture error of a workpiece based on the comparison inspection between Industrial Computed Tomography（ICT） images and Computer Aided Design（CAD） model was discussed.Firstly,the edged surfaces of ICT images were extracted by the Cellular Neural Network（CNN） with adaptive templates and the data were fused in three directions to obtaine the complete 3D edge surfaces.Then,the Principal Component Analysis（PCA） with the method of minimum bounding box were combined to perform a rough registration,and Singular Value Decomposition and Iterative Closest Point（SVD-ICP） algorithm were used to realize the refined registration for the edged surface data and the CAD model.In experiment,the k-d tree was used to improve the calculation speed of searching for the closest point.The experimental results validate that the comparison inspection method is automatic,visualized and high-accuracy.By the improved comparison inspection method for ICT images and CAD model,the ICT technology can be used to analyze and improve the manufacturing process.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2011年第10期2533-2540,共8页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.60972104)
重庆市自然科学基金资助项目(No.2010BB4222)
关键词
计算机断层成像
计算机辅助设计
神经网络
迭代最近点
比对检测
industrial Computed Tomography（CT）
Computer Aided Design（CAD）
neural network
iterative closest point
comparison inspection
