| With the rapid development of high-end manufacturing industries such as aerospace,automobiles,and ships,the demand for precision gears continues to increase.CNC gear hobbing machines are the main equipment for mass production of gear roughing,and hobs are key components that affect their processing efficiency and quality..In the process of gear hobbing,hob wear is inevitable.With the continuation of hobbing,it will inevitably lead to serious hob wear after a period of time,which cannot continue to meet the gear processing quality and accuracy requirements.At this time,the hob must be replaced.But changing the tool too early will greatly increase production costs and waste production time;if the tool change is delayed,a large number of workpieces will be unqualified,and the temperature of the machine will be too high,which will damage the machine.Therefore,real-time monitoring of hob wear status and timely tool change or sharpening decisions are urgent problems to be solved,and there is currently a lack of research on hob wear monitoring of machine tools.In response to the above problems,this paper studies the hob wear status recognition based on the vibration characteristics of the hob spindle.The main research contents of the thesis are as follows:(1)Introduced the principle of gear hobbing and the mechanism of hob wear,and designed an experiment plan for data collection of gear hobbing.According to the surface images of the hob at different wear periods collected during the experiment,the hob wear form was analyzed,and the hob spindle vibration signal was collected as the data support for the analysis of the hob wear status,which lays the foundation for the recognition of the hob wear status.(2)Propose a combined threshold denoising method with variational modal decomposition.A simulation example verifies the superiority of the VMD decomposition method in the near-frequency signal decomposition,and calculates the theoretical value of the effective vibration frequency during the gear hobbing process.For the problem that the mode number K is difficult to determine in the VMD decomposition method,the center frequency index and energy are used.The entropy index optimizes the K value and verifies the effectiveness of the optimization results.According to the correlation coefficient method,the relevant modal components are determined,and the soft threshold and hard threshold noise reduction are performed respectively.The superiority of the noise reduction effect of the proposed method is verified by comparison with the wavelet threshold method.(3)The method for extracting the Z-direction vibration characteristics of the hob spindle based on the sensitivity analysis is discussed,and the improved model of DE optimized LS-SVM parameters is used to identify the wear state.Based on the Z-direction vibration characteristics of the hob spindle,the time domain,frequency domain and time-frequency features are extracted,and the Laplacian score and compensation distance evaluation method are used to analyze the feature sensitivity,and the weakly related features are eliminated,and then the PCA is used to reduce the dimensionality.Six-dimensional feature vector.Aiming at the problem that the penalty factor of least squares support vector machine and the width of the kernel function are difficult to determine,the DE algorithm is used to optimize the parameters,and the recognition effects of the new model and the original model are compared to verify the effectiveness of the proposed method.(4)Developed the basic functions of gear hobbing and the hob wear monitoring module.Aiming at the deficiency of the Huazhong 8 CNC system that does not have the gear hobbing function,the basic functions of gear hobbing and the tool wear monitoring module are developed,the human-machine interface is designed,and the NC code and program call control program for CNC machining are compiled.The field test verifies the feasibility of the developed functional module. |
