Research On Key Technology Of Vision-Based Tool Geometric Parameter Measurement

In the modern processing and manufacturing field,the requirements for product quality are getting higher and higher,and the geometric parameters of the tools used in the production and processing process have an important influence on the machining accuracy.At present,there are many kinds of tool testing equipment on the market in China,but it is generally a manual mode,which is difficult to achieve automation and intelligence,and it is difficult to meet the requirements of high-precision machining.Aiming at these problems,this paper designs a vision-based tool parameter detection device,which realizes the non-contact measurement of tool parameters and the human disturbance factor,which can effectively improve the tool detection efficiency and accuracy.This paper firstly designs a mechanical structure of vision-based tool detection equipment,including mobile unit,vision unit and spindle unit.Based on the designed mechanical mechanism,a set of tool detection teaching process is developed,which can realize different tools.The automatic detection of the type lays the foundation for the subsequent visual inspection of the tool geometry parameters.Secondly,aiming at the visual unit structure designed in this paper,the method of bright field diffused backlight illumination is designed,and the influence of light intensity variation on measurement accuracy is analyzed.A dynamic adjustment of pixel equivalent to the error caused by light intensity variation is proposed.make up.Aiming at the problem of focusing the focus of the tool image,a method of measuring the sharpness of the image by using the gray gradient is proposed.When the ROI region is of interest,the spindle is rotated.When the gradient is maximum,the tool image is the clearest and a good focusing effect is achieved..Thirdly,it is difficult to locate the contour feature points of non-ideal tools.A method of global contour fitting is proposed.The conditions for the overall fitting of the contours are analyzed,and the contours that are not satisfied are spatially transformed.The feature points are regarded as unknown.Optimization,transforming the piecewise fit to the overall nonlinear fit.The initial value of the feature point coarse positioning is taken as the initial value of the nonlinear equation iteration.The feasibility and accuracy of the algorithm are verified by comparing the residual of the actual contour and the fitted contour,and the accuracy and efficiency of the algorithm are effectively improved.Finally,in order to further improve the automation and intelligence of tool detection,an algorithm for automatically identifying classification tools is proposed.This algorithm learns the shape of the tool by training,quickly classifies the tool types,simplifies the operation flow,and has higher detection efficiency.