Research On Grinding Wheel Wear Condition Monitoring Of Multi-sensor Fusion

In the process of grinding,the degree of abrasion of the grinding wheel directly affect the quality and processing precision of the processing surface.In order to improve the grinding precision and accurately determine the proper dressing cycle of the grinding wheel,this paper systematically studies the The research status of the grinding wheel wear monitoring system based on the theory analysis and experimental research to establish a multi-sensor fusion wheel wear monitoring system.The main research contents of this dissertation include:Aiming at the acoustic emission signal generated by grinding process,by analyzing the advantages and disadvantages of the acoustic emission signal processing method systematically,the wavelet packet analysis and energy coefficient analysis method suitable for grinding machining are determined.The acoustic emission signal during the grinding process Analysis of experimental study,identified suitable for monitoring the status of grinding wheel acoustic emission signal layer is 7 layers.Aiming at the on-line monitoring and monitoring technology of grinding wheel wear,the influence of feed rate on grinding force and surface quality after grinding was analyzed.The wheel wear detection method was established by super-depth-depth synthesis algorithm to detect the wheel shape before and after wear.Based on the study of the influence of grinding wheel wear state and grinding parameters on acoustic emission signal,grinding force and grinding temperature,an acoustic emission monitoring system,grinding force monitoring system and Grinding temperature monitoring system composed of multi-sensor fusion wheel wear condition monitoring system.According to the wheel grinding experiment,the influence law of the worn state of grinding wheel on acoustic emission signal,grinding force and grinding temperature is analyzed.According to the test results,the threshold value needed to monitor the worn state of grinding wheel is established.Based on BP neural network,-2-2 grinding wheel wear identification system was tested on the wheel wear test.Through the training and testing of the sample data,the test proves the feasibility and accuracy of the wheel passivation condition monitoring.