Tool Wear Status Monitoring Based On Scorpion Vibration Sensing Mechanism

The real-time monitoring of tool wear status is a key technology of advanced manufacturing systems and an important part of machining.Effective tool status monitoring is of great significance for improving production efficiency,reducing production costs,and improving product quality.However,in the actual application process,the existing tool wear condition monitoring equipment often has the problems of insufficient detection accuracy,huge equipment,expensive cost,and large workload of preliminary data calibration.Therefore,this article analyzes the current research status of tool condition monitoring technology.Starting from the vibration signal,the monitoring of the running state of the tool is researched,and the main work content is as follows:Firstly,a tool vibration signal detection system based on a new bionic flexible vibration sensor element was built.The new bionic flexible vibration sensitive element is a sensing structure that mimics the sensing mechanism of the scorpion seam receptor,and has the advantages of high detection accuracy and low manufacturing cost.Considering the influence of the internal processing environment of CNC machine tools in industrial testing,the packaging device is designed according to the structure of the bionic flexible vibration sensitive element,and the rigid bionic sensor is packaged to enable it to detect tool vibration signals accurately and in real time during machine tool processing.Set up a signal acquisition system,upload the tool vibration signal to the host computer for storage,and record the real-time tool status label during the real-time detection process.Secondly,this thesis uses the support vector machine algorithm to diagnose the tool's normal working and flank faults on the collected tool vibration signals.First,use wavelet packet denoising to remove the environmental noise in the tool vibration signal,and then normalize the signal to extract the time-domain and frequency-domain features of the signal,mainly including maximum value,mean value,variance,margin coefficient,pulse factor,center of gravity Frequency etc.,After that,the support vector machine algorithm is used to train the tool vibration signal sample characteristics,and the tool fault diagnosis model is established.The experimental results verify the effectiveness of the tool vibration signal detection system based on the bionic sensor proposed in this thesis.Finally,this thesis realizes the analysis of tool wear status through an improved parameter-free K-means clustering algorithm.The traditional K-means algorithm needs to manually determine the parameters,and the random selection of the initial clustering center will lead to the local optimal problem.Combined with the clustering algorithm based on the density peak,an improved algorithm that can automatically determine the number of clusters and the clustering center is proposed.K-means algorithm without parameters.First calculate the density and dispersion of each sample point and establish a decision diagram.Then,according to the location information of the decision diagram,calculate the average distance between the sample point and its neighbors,and determine the cluster center and cluster number according to the distance distribution law of the data points.The selected cluster center and the number of clusters are used as the cluster center and cluster number of the K-means clustering algorithm to realize the improvement of the parameter-free K-means algorithm.And on the Gaussian data set and UCI data set,the effectiveness and feasibility of the algorithm are verified.Finally,the improved algorithm proposed in this thesis is used to cluster machine tool vibration signal data to realize tool wear status recognition.In summary,this thesis has carried out a research on tool wear status monitoring based on the scorpion vibration sensing mechanism,and encapsulated a new type of bionic flexible vibration sensitive element to detect tool vibration signals,which improved the detection accuracy and reduced the detection cost;support vector machine algorithm is used to diagnose the tool vibration signals in two states: normal working and flank faults;an improved parameter-free K-means algorithm is proposed for tool wear status monitoring,which can identify the running state of the tool according to the change characteristics of the tool state,and while obtaining a better tool state recognition result.It has high practical application value for accurately and real-time extracting the running state of CNC machine tools.