Abstract:Based on the traditional tool life prediction formula， the main influencing factors of tool life are determined through the analysis of the influencing factors of tool life， and a tool life prediction model based on BPRBF neural network is established; the tool life data samples of experiments are counted， the least square method is used to nonlinearly fit the mathematical model for establishing the experimental tool life model.The BPRBF neural network model and the traditional BP neural network model are tested and simulated through a 10fold crossvalidation method， and the tool life data samples are combined to verify the established tool life model.By comparing with the traditional BP neural network model and tool life prediction model， the BPRBF neural network has higher prediction accuracy， and the prediction model is effective in tool life prediction.