【摘要】 通过分析总结影响刀具寿命的主要影响因素,建立铣削刀具加工参数与刀具寿命的径向基神经网络模型。训练模型使用了10组样本数据,以刀具直径、铣削速度、铣削宽度、铣削深度、进给量、刀具齿数作为网络输入参数,采用十折交叉验证方法对所构建模型进行验证,能够对刀具寿命进行较为准确的预测。与传统BP神经网络模型比较发现,径向基神经网络具有更好的预测精度和稳定性,是预测刀具寿命的一条有效途径。更多还原

【Abstract】 By analyzing and summarizing the main influencing factors of tool life, a radial basis neural network model for machining parameters and life of milling tools is established. The training model uses 10 sets of sample data. The tool diameter, milling speed, milling width, milling depth, feed and tool teeth number are used as network input parameters. The ten-fold cross validation method is used to validate the model and the tool life can be accurately predicted. Compared with the traditional BP neural network model, the RBF neural network has better prediction accuracy and stability, and is an effective approach to tool life prediction.更多还原