摘要
从放在工件夹具上的声发射(AE)传感器测得的磨削加工中的AE信号中,提取有关磨削表面粗糙度的信息,用神经网络的方法对高速深切平面磨削工程陶瓷部分稳定氧化锆的工件表面粗糙度进行了在线连续监测.结果表明,该方法基本可行,通过进一步改进,可以用于高速深切平面磨削工程陶瓷工件表面粗糙度的在线监测.
An on-line monitor method for grinding surface roughness based on theory analysis and test study is presented, which collects the information of grinding surface roughness from the acoustic emission （AE） signal produced by the process of high speed deep grinding of engineering ceramic PSZ to realize the on-line intelligent detection and prediction for grinding surface roughness by the Feed forward Backpropagation Neural network. The reliability and feasibility of the method is proved by actual test and simulation. The result indicates that after it is improved, this method will be good for monitoring grinding surface roughness.
出处
《湖南文理学院学报（自然科学版）》
CAS
2010年第1期40-43,共4页
Journal of Hunan University of Arts and Science(Science and Technology)
关键词
表面粗糙度
神经网络:声发射
高速深磨
工程陶瓷
surface roughness
neural network
acoustic emission
high speed deep grinding
engineering ceramic.
