论文信息 - Automatic Machining Sequence Determination Based on Physical Models and Artificial Intelilgence Techniques

Automatic Machining Sequence Determination Based on Physical Models and Artificial Intelilgence Techniques

Abstract In this paper a methodology for generating the machining sequence for prismatic parts based on physical models and Artificial Intelligence techniques is proposed. This work is particularly developed for intersecting features where the machining quality of one feature might be adversely affected due to the presence of other features. The idea of integrating the physical models into the process planning activity is to build in surface quality considerations at the early planning stage. A heuristic graph search strategy is used to generate and search the process planning graph from the blank to the final part configuration.

[1] W. Kline,et al. The Prediction of Surface Accuracy in End Milling , 1982 .

[2] John W. Sutherland,et al. An Improved Method for Cutting Force and Surface Error Prediction in Flexible End Milling Systems , 1986 .

[3] Hong-Chao Zhang,et al. Computer Aided Process Planning: the state-of-the-art survey , 1989 .

[4] Paul K. Wright,et al. Automating process planning: Using feature interactions to guide search , 1989 .

[5] Richard A. Wysk,et al. Intelligent reasoning for processing planning , 1987 .

[6] Ying-Yi Lee. Simulation of dynamic behavior and chatter in end milling with an improved model , 1992 .

[7] Ravi O. Mittal,et al. A “micro” process planning system based on integer programming for prismatic parts produced on horizontal machining centers , 1989 .