Using Hopfield neural networks for operational sequencing for prismatic parts on NC machines

A numerical control (NC) machine is accurate and expensive equipment that p rovides us with flexible and reliable operations. However, many process pla nners only use their instinct in planning operational sequencing and do not minimize non-cutting time. In this paper, the sequencing task is formulate d as constrained optimization problems to generate efficient machining of a part for NC machines. Factors in this study include part and table orienta tions and feature grouping for same cutting tools. First, this proposed met hod finds the minimum part orientations to cover all part features in order to reduce the most time consuming setups. Then it finds the minimum table orientations needed based on the accessibility of parts features in each pa rt orientation. Most importantly, the preliminary sequence is refined by in cluding feature precedence relationship and feature clustering for tools an d tool approaching directions that will reduce tool re-orientation and tool changing time. Due to potential conflicts of constraints for sequencing op timization from the imbedding of precedence relationships, the soft computi ng ability of neural networks must be utilized in this refining procedure. This paper models the problem that allows an analogy to be conducted betwee n finding the best operation sequence and minimizing the energy function of a Hopfield neural network. Finally, a spindle cover is used as an example to illustrate the implementation of the proposed method. (C) 2001 Elsevier Science Ltd. All rights reserved.