Research On The Wear State Of Directional Internal Cooling Milling Tool Based On Machine Vision

Nickel-based superalloy materials have the advantages of high strength,good heat resistance,and strong corrosion resistance.It is widely used in the aerospace field.However,due to the difficult-to-machine property,the tool wear is seriously when using traditional external casting cooling processing,and the surface of the workpiece The machining quality is difficult to guarantee.Therefore,the text adopts the directional internal cooling method to accurately spray the coolant to the cutting area through the internal runner,which effectively reduces the temperature of the cutting area and extends the tool life.In addition,in actual processing,frequent tool changes increase machine downtime and blade utilization,which significantly increases processing costs and reduces processing efficiency.Monitor the tool wear during processing,timely feedback the tool wear to the machine tool and the operator,and realize the tool wear status diagnosis.This not only effectively ensures the tool utilization and processing quality,reduces machine downtime,and reduces production costs,but also Provide an effective guarantee for the realization of automated intelligent processing.This paper adopts the method based on machine vision to monitor the tool wear status,builds a vision system to realize the acquisition of tool flank image and workpiece surface texture image,and extracts flank surface features and workpiece surface texture features through image processing algorithms.Compared with the actual wear of the detected tool,the accuracy detection of the tool condition monitoring system in this paper is realized.The main research contents of this article are as follows:（1）According to the tool wear form,tool wear process and international bluntness standards,determine the later tool face wear widths VB,VB_max and flank wear area AVB as the blunt standards of this article.According to the functional requirements of the system,the selection and design of industrial cameras,optical lenses,lighting systems and other hardware are realized,and a visual monitoring platform is built to realize the collection of high-quality workpiece surface images and tool surface images in complex processing environments;（2）The design of the image texture analysis module on the surface of the workpiece is realized,and the feature description of the surface texture of the workpiece is realized by the Gray-Level Co-Occurrence Matrix（GLCM）and the Local Binary Pattern（LBP）.The law between texture features and tool wear is analyzed,and the tool status diagnosis based on GLCM is realized;on the other hand,the support vector machine model is used to classify the histogram features calculated by the local binary mode method,and the LBP-based Classification of tool wear status;（3）The design of the tool state analysis module is realized,and the image registration is realized through the Structural SIMilarity detection（SSIM）algorithm,which effectively solves the problem that the flank of the tool is not facing the CCD（Charge Coupled Device）camera when the tool image is collected.Problem;at the same time,in order to extract the flank wear value,this paper uses image processing algorithms to process the collected tool images,and extracts three characteristic values of flank wear width VB,VB_max and flank wear area AVB,which are Visualize and automate the tool status inspection process,integrate the algorithm into the Graphical User Interface（GUI）,and improve the degree of automated inspection;（4）A high-speed milling experiment of nickel-based superalloys was carried out.The flank wear value measured by the vision system was compared with the true wear value of the flank surface measured under the microscope,and the detection accuracy of the tool condition monitoring system in this paper was compared.Finally,the tool life under the condition of directional internal cooling is explored.