Research On The Tool Wear State Monitoring Based On The Surface Texture Feature Of Workpiece

Tool wear State Monitoring is very important in cutting,and it directly affects the dimensional accuracy and surface quality of the workpiece.An optical microscope is usually used to directly measure tool wear.This method requires the removal of the tool and is timeconsuming.The tool wear detection method based on machine vision can realize on-machine detection and improve detection efficiency.In this paper,the tool wear detection method is researched based on the surface texture characteristics of the workpiece.Through experiments,the surface image data of the workpiece is collected and the texture features in the image are extracted.The decision tree model in machine learning is used to classify the texture feature data set to realize the tool wear status.Detection.The studies in this thesis can be summarized as follows:(1)Establish an image acquisition experiment and preprocess the sample images.First,the basic framework of the experiment was built according to the experimental principle,and the experimental platform and its main processing parameters were determined.Then,the main components of the acquisition system,industrial cameras,lenses,light sources,etc.,were compared and selected,and the milling tool wear detection methods were studied.Carry out collection experiments and make labels for the collected images,and finally perform preprocessing operations such as sample expansion and image enhancement on the images.(2)Research the tool wear mechanism and texture feature extraction method,extract the Tamura texture feature and analyze the feature attributes.First,determine the specific wear value range corresponding to each wear stage according to the wear mechanism,and digitize the corresponding wear stage tags.Then,based on the research and comparison of image texture feature extraction methods,the Tamura texture feature was selected,and the MATLAB program was written to extract the 6-dimensional feature attributes.Finally,based on the analysis of the distribution map of each characteristic attribute,the characteristic attribute data set is made by eliminating the attribute of rule degree with poor classification.(3)Establish a decision tree classification model based on texture features,and optimize the parameters of the model to improve the classification accuracy of the model.First,based on the principle and structure of the decision tree model,the algorithm flow of common decision tree algorithms is introduced and compared.Then,by dividing the feature data set into a training set and a test set,using the training set to initially train the model to obtain an accuracy of 100%.At this time,the accuracy of the test set is only 71.61%,and the decision tree is over-fitting.Then use grid search and cross-validation to optimize the decision tree parameters and other pruning operations to prun the decision tree to reduce the over-fitting problem,analyze its fitting curve and confusion matrix,and the data fitting effect becomes better after the parameter optimization.At this time,the fitting rate of the training set reaches 100%,and the accuracy of the test set is 89.20%.