Online Orientation Recognition of Single-Crystal Diamond Tools in the Process of Indexing Grinding Based on HMM and Multi-Information Fusion

Abstract

Single-crystal diamond tools occupy an important position in the field of optical processing as the basis and key to advanced optical manufacturing technology, such as grating manufacturing and optical mirror-turning processing. Single-crystal diamond tools have become the cornerstone of the development of related industries. This paper takes a single-crystal diamond arc tool as the research object. Sound signal analysis technology and vibration signal analysis technology are comprehensively applied to the online orientation identification of a single-crystal diamond tool in the indexing grinding process. The online orientation method of the tool is explored, the sound signal and the vibration signal are taken as the characteristic signals, and a wavelet algorithm (WT) is used to reduce the noise of the vibration signal and sound signal. The kurtosis of the sound signal and the kurtosis and skewness of the vibration signal in the high-order statistics strongly related to the grinding direction of a single-crystal diamond are used as the characteristic parameters, and the online direction recognition model of the tool is established using the Hidden Markov Method (HMM). The above characteristic parameters are used as model input for multi-information fusion. The mapping relationship between the characteristic parameters of the characteristic signal and the crystal orientation of the single-crystal diamond crystal face is obtained, and then the online orientation method of the single-crystal diamond arc tool in the process of indexing grinding is formed. The effectiveness of the method is verified by experiments, and effective orientation information is provided for research on the positioning control strategy of the tool grinding process to ensure the efficiency of grinding and improve the manufacturing level of the tool.