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

• Published in: Engineering applications of artificial intelligence 2001-06, Vol.14 (3), p.357-368
• Main Authors: Chang, C.Alec, Angkasith, Vorapoch
• Format: Article
• Language: English
• Subjects: Hopfield neural network; Numerical control; Operational sequencing; Prismatic parts; Process planning
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/S0952-1976(01)00013-6

A numerical control (NC) machine is accurate and expensive equipment that provides us with flexible and reliable operations. However, many process planners only use their instinct in planning operational sequencing and do not minimize non-cutting time. In this paper, the sequencing task is formulated as constrained optimization problems to generate efficient machining of a part for NC machines. Factors in this study include part and table orientations and feature grouping for same cutting tools. First, this proposed method 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 part orientation. Most importantly, the preliminary sequence is refined by including feature precedence relationship and feature clustering for tools and tool approaching directions that will reduce tool re-orientation and tool changing time. Due to potential conflicts of constraints for sequencing optimization from the imbedding of precedence relationships, the soft computing ability of neural networks must be utilized in this refining procedure. This paper models the problem that allows an analogy to be conducted between 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.