2021 6th International Conference on Modern Machinery Manufacturing and Materials Engineering (IC4ME 2021)
Prof. Li Guo, Hunan University, China


Prof. Li Guo

Hunan University

Research Experience:

He is an academic backbone of Hunan University, a senior member of Chinese Mechanical Engineering Society, a member of National Science and Technology Ministry, a reviewer of National Natural Science Foundation, a reviewer of China Science and Technology Journal of China Science and Technology Information Institute of Ministry of Science and Technology, a reviewer of science and technology projects and science and technology awards of National Ministry of Science and Technology and Ministry of Education, a reviewer of science and technology awards of Hunan Province, Shandong Province and Zhejiang Province, a reviewer of natural science awards of Hunan Province, Zhejiang Province and Shandong Province, etc. He is an expert in the review of the National High Efficiency Grinding Engineering Technology Research Center, and an academic backbone of the National 985 High Technology Research (Automotive Advanced Design and Manufacturing Innovation Team). He is a member of the editorial board of the National Chinese Science and Technology Core Journals "Precision Manufacturing and Automation" and "Mechanical and Electrical Engineering". He is also a reviewer of International Journal of Advanced Manufacturing Technology, and a reviewer of National Journal of Vibration Engineering, Journal of Hunan University, and Engineering Mechanics.

Speech Title:

Research on acoustic emission intelligent monitoring in grinding engineering ceramics


Acoustic emission (AE) signal analysis by use of short time Fourier transform is used to monitor the grinding heat by ues of laser. The relationship between the acoustic emission signal of high speed grinding of engineering ceramics and grinding force, grinding temperature are studied. High precision AE monitoring of diamond grinding wheel wear in engineering ceramics grinding were carried out. The variance of wavelet decomposition coefficient of AE signal in alumina grinding is used as the input feature of support vector machine. The empirical mode decomposition (EMD) of grinding AE signal is used to extract the effective value, variance and energy coefficient of its intrinsic mode function as the input features of least squares support vector machine. The optimized BP neural network is used to monitor the grinding surface roughness with high precision by use of AE. The research solved the problem of acoustic emission monitoring in engineering ceramics grinding and laid the foundation for its practical application!