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 in automating and integrating design and manufacturing for ef®cient production, its off-line properties prohibit the shop ̄oor controllers from rapidly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop ̄oor controllers determine cutting parameters in real-time based on shop ̄oor status. At off-line is the dynamic planning model constructed as a neural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop ̄oor controllers to determine the cutting parameters. A prototype system is constructed to validate whether the dynamic planning model is capable of determining dynamically and ef®ciently the cutting parameters for a particular set of shop operating factors. Owing to the dynamic planning model, the shop ̄oor controller will increase ̄exibility and robustness by rapidly and adaptively determining the cutting parameters in unexpected errors occurring.