基于神经网络的智能刀具状态检测系统
佚名
【期刊名称】《南京航空航天大学学报（英文版）》 【年(卷),期】2000(017)002
【摘要】Reliable on-line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunc tion with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back-propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on-line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool-wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for online monitoring of tool wear states and abnormalities.%可靠的在线刀
基于神经网络的智能刀具状态检测系统
