Research Of Woodworking Milling Cutter Wearing Detection Based On Machine Vision

The wearing of woodworking tools is an important factor affecting the machining accuracy and surface quality of furniture parts.At present,the tool replacement for furniture production equipment is based on the qualitative judgment made by technical personnel to observe the tool wear.However,influenced by subjective factors such as vision and experience,people have different standards for tool wear judgment.Therefore,it is necessary to establish a unified tool wear evaluation method.This study has designed a woodworking milling cutter wearing detection system by using machine vision recognition and measurement technology.The main contents are as follows:Firstly,according to the cutting principle and the wear form of the milling cutter,combined with the detection ability of the machine vision technology,the detection index of identifying the broken edge and measuring the residual wear of the back blade is determined.According to the machine vision theory proposed by David Marr,the operation mechanism,functional modules and technical route of the detection system are determined.Secondly,according to the practical needs of precision,this research determines the configuration of the camera,lens and light source of the detection system.The formula of selecting the camera and lens type is deduced based on the woodworking tool size.After the imaging system is calibrated,the image distortion of the milling cutter is corrected,and the size mapping relationship between the real world and image pixels is established.In order to ensure the accurate positioning of the measuring plane on the shooting plane after the turning of woodworking milling cutter,the method of controlling the cutter position by laser positioning was studied.Thirdly,the algorithm of image enhancement and smoothing was studied to eliminate the interference of noise and wood powder and particles adhered on the surface of milling cutter to wear detection.Through the method of Blob analysis and shape template matching,machine vision gains the ability to recognize the type of milling cutter,and realize the function of automatic positioning and extracting the front and back cutter surface of the tool.In addition,based on the gray and size characteristics of the front blade,the front blade broken edge is identified and the amount is output.The edge detection algorithm is used to extract the edge of the back blade and establish the measuring datum,and the vertical line of measuring datum is established to get the intersection point with the blade.The amount of the residual wear of the back blade is obtained by calculating the distance between the intersection point and the vertical foot.Furtherly,by using the algorithm of measuring the residual wear of the back blade,Vernier caliper calibration distance is measured to verify the accuracy of the detection system,and the width of unworked woodworking milling cutter's back blade is measured to verify the accuracy of the wear detection algorithm.