【摘要】 针对微钻头的圆角、外径等的检测,研究了微钻头棱边投影的拟合方法。首先,采用印刷电路板(PCB)微钻头高精度自动化检测系统采集图像。在对微钻头棱边投影的椭圆方程进行拟合时,以采样点坐标的组合矢量坐标到超平面距离的平方和为目标函数。然后,根据求解目标,设计一个有侧向连接的Sanger网络,以采样点坐标组成的矢量坐标为Sanger网络的输入。最后,采用自适应次分量的提取方法,以自相关矩阵的最小特征值所对应的特征向量为超平面的拟合系数,即椭圆方程拟合系数,得到微钻头的圆角、外径等技术指标。这种自适应Sanger网络在工件形状误差检测中的应用方法能满足微钻头检测的精度要求。更多还原

【Abstract】 To measure the rounded corner and diameter of a micro-drill,the fitting of the elliptical equation for margin projection of the micro-drill was researched.Firstly,the margin projective image was collected with a high precise automatic test system of PCB micro-drill.While the elliptical equation of margin projection of micro-drill was fitted,the sum of square distance from the sample points’ vector coordinates to the hyperplane determined by the elliptical coefficients was taken as an objective function.Then,a Sanger neural network with lateral connection was designed,in which the input signal were vector coordinates composed of sample points.Finally,on the basis of an adaptive minor component extracting method,the eigenvector of minimum eigen values was taken as the fitted coefficients of the hyperplane,i.e.,fitted elliptical coefficients,and the rounded corner and diameter of the micro-drill were obtained.The proposed approach can meet the precision requirements of micro-drill testing,and is a novel application of adaptive Sanger network to measuring shape errors.更多还原