Machine Vision-Based Measurement of Deflection Angle in Automotive Press-Fit Bushings
The angle measurement of trim panel installation is considered crucial for ensuring the accurate assembly of automotive suspension with the trim panel during the pressing process.Existing monocular measurement methods have been difficult to widely apply due to limitations imposed by the camera mount-ing environment and the low measurement accuracy.In order to enhance the measurement accuracy of trim panel rotation angle,a method based on feature matching and radial basis function neural network was pro-posed.The ORB algorithm was optimized using the Hessian matrix to eliminate mismatched pairs and im-prove the matching performance.A baseline template matching strategy was employed to address the issue of image feature occlusion caused by camera oblique view,and the feature points collected from images were transformed onto the baseline template.By introducing a radial basis function neural network for rota-tional angle soft measurement,the nonlinear relationship between feature points and rotation angle was fitted to enhance the accuracy of trim panel deflection angle measurement.Experimental results demonstrated that the proposed method effectively performed angle measurement,with a maximum average relative error of 2.72%,meeting the requirements of trim panel deflection angle measurement,and possessing certain appli-cation value in the automotive production process.
feature matchingORB(oriented fast and rotated brief)homography transformationHessian matrixRBFNN(radial basis function neural network)
万方数据
维普
