Research On Multi-Parameter Monitoring And Quality Analysis System For State Of Ring Groove Grinding

The quality of ring groove is important to a bearing, which determines the service performance and lifetime. Grinding is the essential process to ensure the quality of a groove, but the domestic groove grinding process lacks quality conformance and modern monitoring tools. It results in the discommodities to the product quality control and management. In order to improve the actuality, this dissertation researchs and developes a multi-parameter monitoring and quality analysis system for the groove grinding. This system monitors four parameters, including cross feed, spindle motor power, grinding wheel vibration and acoustic emission, and build the relationshp between the features of the four parameters and the quality of groove grinding based on BP neural network. The application of this system can remarkably enhance the automation level and precision of the quality monitoring of groove grinding.The whole dissertation is divided into six chapters:Chapter one brings out the background of the research and summarizes the actualities and trends of the grind monitoring methods. Based on that, the necessity of research on the subject of this dissertation is introduced. The content of this dissertation is gived in the end.Chapter 2 discusses some key process of groove in the beginning. Furthermore, factors in the grinding process which influence the quality are analyzed in particular. On the basis of these works, monitoring strategy of the system is represented, which provides a theoretical foundation to establish an overall system followed up.Chapter 3 discusses the bus techniques of measuring system, and establishs the general scheme of the system based on PC-DAQ. Then, designing of the system hardware is described in detail.Chapter 4 analyzes function requirement in the quality analysis system, and introduces the software developing environment and some key techniques. Besides that, implementation methods and theoretical foundation of the main function modules are represented in particular, such as the signal acquisition and storage, data processing and analyzing, status diagnosing and distinguishing, etc.Chapter 5 uses the quality analysis function to analyze a group of testing data measured by the system; furthermore, the features of the signals are extracted and optimized. A mapping relationship between signal features and groove grinding quality status is established based on BP neural network. In the end, verification is taken to prove the validity of the whole system.Finally, in Chapter 6, a conclusion is made and the further research works is prospected.