Milling Cutter State Analysis Of Neural Network Model And Simulation

Along with automation in manufacturing industry, many industry problems have been generated and include tool wear and failure. The operate-person observed tool and replaced it in the early period of machining. However, in automatic manufacturing system, the tool-failure causes invalid of machine function and the whole system's failure. Therefore, the prediction of tool wear and failure is very urgent.In this paper, the condition analysis of shape-milling tool is set up based on artificial neural network. First of all, the conditions are classed and kinds of features are described and influence factors are detailed analyzed. Moreover, the feature physical parameters are selected in view of four typical conditions of shape-milling tool, namely acoustic emission signal, cutting force signal and power signal.The neural network can analyze shape-milling tool condition because of better nonlinear approachable and generalization ability. It diagnoses function through serious training of learning samples. The neural network model is set up and analyzed. The model can use to analyze other milling tool condition due to expand. The simulation graphs are drawn by means of MATLAB neural network toolbox.The research provide neural network model of shape-milling tool condition. The model is completely possible to analyze shape-milling tool condition, Because neural network construction's expand, it can be used in other tool condition. The model improves machining quality and efficiency.