Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning

Although feature-based computer-aided process planning plays a vital role i n automating and integrating design and manufacturing for efficient product ion, its off-line properties prohibit the shop floor controllers from rapid ly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop floor controllers determine cutting parameters in real-time based on shop floor status. At off-line is the dynamic planning model constructed as a ne ural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop floor controllers to determine the cutting parameters. A prototype system is constructed to validate whet her the dynamic planning model is capable of determining dynamically and ef ficiently the cutting parameters for a particular set of shop operating fac tors. Owing to the dynamic planning model, the shop floor controller will i ncrease flexibility and robustness by rapidly and adaptively determining th e cutting parameters in unexpected errors occurring.