RBF云神经网络在数控机床刀具磨损状态识别中的应用

Application of RBF Cloud-neural Network in Identification of CNC Machine Tool Wear

在数控机床加工过程中,刀具破损是造成加工设备损坏和加工安全事故的主要起因,且刀具的磨损对加工质量有着直接影响,因此,正确对数控机床的刀具状态进行识别有着重要的工程价值。提出一种云理论与RBF神经网络相结合的RBF云神经网络模型,该模型既有云理论的随机性和模糊性,又有RBF的学习、记忆能力。将其应用到数控机床的刀具磨损状态识别中,实验结果表明:该网络模型的精确度较高,具有较强实用性。

In the NC machining process, tool breakage is the main cause of equipments damage and safety incidents, and tool wear has a direct impact on the quality of product, therefore, correctly identifying NC machine tool state has important engineering value. A RBF cloud-neural network model which combined cloud theory and RBF neural network was presented. The model had not only randomness and fuzziness of cloud theory, but also the ability of learning and memory of RBF. It was applied to the state identification of NC machine tool wear. The experimental results show that the model has high accuracy and strong practicability.