Research On Milling Cutter Wear Detection Method On Machine Based On Line Laser Detection And Machine Vision

With the increasing demand of intelligent manufacturing process automation and information technology,efficient automatic detection technology for tool wear status has become a recognized key technology.Among them,machine vision technology is the main method of non-contact tool wear detection,but due to the light and the complex spatial structure of the measured object and other factors on the industrial camera interference is inevitable,directly affect the effectiveness of its detection results.Therefore,it is necessary to explore the tool wear detection technology based on the fusion of multiple sensors.In this paper,machine vision-line laser dual sensors are used to study the wear state identification of milling cutter side edge.The contents include the detection principle of dual sensors,experimental system construction and data acquisition process formulation,and the technology of identification and evaluation of end milling cutter wear state based on fusion of multi-source information.The specific research is as follows:(1)A tool wear detection method based on machine vision and linear laser dual sensors is proposed.Based on machine vision technology,edge detection data of line laser is used to supplement the missing information of machine vision technology,providing multi-source information for tool wear status recognition.Combined with their respective detection principles to determine the specific experimental scheme.(2)A tool wear detection system based on dual-sensor fusion was designed.According to the design of the acquisition system,the hardware selection,initial state calibration,diameter data calibration and other problems in the detection system were determined,and the acquisition process of dynamic acquisition image data and diameter data of end milling cutter side wear was developed.(3)The method of tool wear identification using multi-source information fusion technology is studied.Firstly,the improved combined thresholding based segmentation algorithm is used to extract the contour of tool wear region.Secondly,the fusion of linear laser data and machine vision results effectively compensates the missing wear information of tool tip area,and achieves more complete tool profile recognition effect and higher tool wear parameter accuracy.Experimental data are used to verify the accuracy of tool wear parameters obtained by this method,and the measurement accuracy of the maximum wear value is greater than 98%,and the maximum error is less than 5 μm.The problem of height calibration in 3d topography restoration of wear area is solved by using line laser data,and the further fusion of multi-source information is realized.(4)A set of multi-dimensional tool wear evaluation index system is established based on the data of multi-source information fusion.The evaluation indexes of three dimensions are realized to evaluate the wear state of milling cutter side edge.At the same time,a multi-dimensional tool wear evaluation index system display platform was designed based on Matlab.Finally,the function and function of the index system are analyzed with typical experimental samples.