An intelligent approach of obtaining feasible machining methods and their selection priorities based on features using neural network

To obtain all feasible machining methods and their quantitative selection priority, an intelligent making decision approach using back-propagation neural network is proposed. Uniform design method, which is adapted for the problem of multiple factors and multiple levels, is adopted to build representative sample sets for the network. The neural network is trained by an improved back-propagation algorithm which can adjust momentum factor and learning rate simultaneously. Linear regression analysis is utilized to test the trained network. A case study has been conducted to demonstrate the effectiveness of the proposed approach.