Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ri...Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.展开更多
针对机器视觉轴承内圈侧面复杂形状尺寸检测精度低的问题,提出根据检测目标建立小面积感兴趣区域(Region of Interest,ROI)的自适应选取方法和基于Zernike矩的ROI亚像素级边缘提取方法,大幅提升了轴承内圈尺寸的检测精度。首先分别拍摄...针对机器视觉轴承内圈侧面复杂形状尺寸检测精度低的问题,提出根据检测目标建立小面积感兴趣区域(Region of Interest,ROI)的自适应选取方法和基于Zernike矩的ROI亚像素级边缘提取方法,大幅提升了轴承内圈尺寸的检测精度。首先分别拍摄轴承内圈左侧与右侧轮廓图像,对图像进行预处理。在此基础上,通过角点检测融合像素扫描的方法实现自适应ROI选取,解决了因轴承内圈移动引起的小面积ROI边缘误判问题;使用Canny算子提取ROI的像素级边缘,再用改进的Zernike矩算法得到亚像素级边缘。最后,分别对ROI中提取的边缘进行最小二乘圆拟合和直线拟合,根据像素当量与视场间隔将图像中各尺寸转换为轴承内圈实际尺寸。实验结果表明:所提方法测量的标准不确定度低于0.005 mm,满足轴承尺寸高精度检测的要求,对于实现轴承检测的自动化有实际意义。展开更多
基金Project(51205299)supported by the National Natural Science Foundation of ChinaProject(2015M582643)supported by the China Postdoctoral Science Foundation+2 种基金Project(2014BAA008)supported by the Science and Technology Support Program of Hubei Province,ChinaProject(2014-IV-144)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(2012AAA07-01)supported by the Major Science and Technology Achievements Transformation&Industrialization Program of Hubei Province,China
文摘Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.
文摘针对机器视觉轴承内圈侧面复杂形状尺寸检测精度低的问题,提出根据检测目标建立小面积感兴趣区域(Region of Interest,ROI)的自适应选取方法和基于Zernike矩的ROI亚像素级边缘提取方法,大幅提升了轴承内圈尺寸的检测精度。首先分别拍摄轴承内圈左侧与右侧轮廓图像,对图像进行预处理。在此基础上,通过角点检测融合像素扫描的方法实现自适应ROI选取,解决了因轴承内圈移动引起的小面积ROI边缘误判问题;使用Canny算子提取ROI的像素级边缘,再用改进的Zernike矩算法得到亚像素级边缘。最后,分别对ROI中提取的边缘进行最小二乘圆拟合和直线拟合,根据像素当量与视场间隔将图像中各尺寸转换为轴承内圈实际尺寸。实验结果表明:所提方法测量的标准不确定度低于0.005 mm,满足轴承尺寸高精度检测的要求,对于实现轴承检测的自动化有实际意义。
