An Intelligent Approach of Obtaining Feasible Machining Processes and Their Selection Priorities for Features Based on Neural Network

To obtain all feasible machining processes and their quantitative selection priorities, an intelligent making decision approach combining back-propagation neural network and backward planning 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 neural network. The neural network is trained by an improved back-propagation algorithm which can adjust momentum factor and learning rate simultaneously, and tested by linear regression analysis. A case study has been conducted to demonstrate the effectiveness of the proposed approach.