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On-Line Monitoring for Grinding Wheel States Based on Neural Network

LIU Gui jie, GONG Ya dong, WANG Wan shan (School of Mechanical Engineer & Automation, Northeastern University, Shenyang 110004, China.  
The acoustic emission signal and the power signal of grinding wheel motor will change when wheel is dulled or broken in grinding process. Using an acoustic emission (AE) sensor and a power sensor as signal recourses,summation of acoustic emission signal increments in fixed time interval and sudden change of power signal of grindiny wheel motor as signal feature values,nonlineer relationship model between signal features and grinding wheel states was built with BP neural network. This model was trained by samples. Based on the model,a system was built which can realize intelligent on line monitoring for grinding wheel states and be a base for intelligent control of grinding machining. The testing results show that the system is workable and can determine the best period for wheel dressing.
【Fund】: 教育部科学技术研究重点资助项目 ( 2 0 0 32 )
【CateGory Index】: TP277
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