基于神经网络的智能刀具状态检测系统

可靠的在线刀具磨损状态检测是柔性制造系统、计算机集成制造系统以及自动化机床必不可少的一个环节。文中论述了用反传神经网络与一类模糊神经网络分析处理由力传感器和声发射传感器所测得的刀具状态信号，识别出刀具的磨损情况，从而进一步实现刀具磨损状态的在线检测，控制自动机床及时更换刀具。本研究对四种规格的钻头的磨损情况进行了全程检测，并比较分析了反传神经网络与模糊神经网络对这一问题的有效性。实验结果表明，这两种方法对处理刀具磨损状态检测均有显著的效果与很高的准确性。用一类模糊神经网络处理多传感器信息是实现刀具状态在线检测的一个极为有效的方法。

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEMUSING FUZZY NEURAL NETWORKS

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.