用于Micro-EDM放电状态分类的多传感器集成与信息融合系统  

Multi-Sensor Integration and Data Fusion System Applied to Discharge Condition Recognition and Classification in Micro-EDM

作　　者：裴景玉[1] 韩静[1] 高长水[1] 刘正埙[1] 

机构地区：[1]南京航空航天大学机电工程学院,南京210016

出　　处：《数据采集与处理》2000年第3期377-381,共5页Journal of Data Acquisition and Processing

基　　金：江苏省航空科学基金;江苏省应用技术基金!(编号 :BJ970 5 7)资助项目

摘　　要：在 Micro- EDM(微细电火花加工 )中 ,由于加工信号的频率高、加工波形的畸变 ,使得常规的用于放电状态检测的方法 ,如电压波形采样、放电延时等 ,已不再适用。利用多传感器的信息融合进行目标识别 ,可以避免单一传感器的局限性 ,减少各传感器不确定性的影响。文中描述了一个用于目标识别与分类的基于模型的多传感器系统。该系统选用以决策层为主的方法 ,以模糊神经网络作为其信息融合的工具。通过实验 ,该系统在正确识别的前提下 ,降低了整个 Micro- EDM系统的成本 ,提高了检测的可靠性 ,体现了多传感器信息融合的优越性。Discharge condition detection methods, such as discharg e time delay, voltage wave sampling, used in the conventional EDM (electric disch arge manufacturing) are not suitable for the Micro-EDM due to the high frequenc y and the distortion of the voltage wave shape. In order to avoid the limitation of a single sensor and to reduce the negative effect caused by the uncertainty of individual sensors, the data fusion of a multi-sensor system is used to acqu ire the relevant knowledge of the target. In this paper, a multi-sensor system is described, which is based on the model tool and applied to target recognition a nd classification. In the data fusion process, a fuzzy neural network (FNN) is selected and used for the data fusion at the report level. Experimental results show that the method works correctly, the cost decreases and the relia b ility of the recognition increases. The superiority of this method has been show n.

关 键 词：电火花加工 放电状态 多传感器 信息融合系统 

分 类 号：TG661[金属学及工艺—金属切削加工及机床]