Optimization of robotic assembly sequences using neural network

This paper presents a neural network based computational scheme to generate the optimize robotic assembly sequence for an assembly product consisting of a number of parts. An assembly sequence is considered to be optimal when it meets a number of conditions: it must satisfy assembly constraints, keep the stability of in-process subassembly, and minimize assembly cost. Currently, various search algorithms have been reported for the purpose, but as the number of the parts increases they often fail to generate assembly sequences due to the explosion of the search space. As an alternative solution to overcome this problem, the authors propose a scheme using both the Hopfield neural network and the expert system. Based on the inferred precedence constraints and the assembly costs obtained from the expert system, the authors derive the evolution equation of the network, and finally obtain an optimal assembly sequence resulting from the evolution of the network. To illustrate the suitability of the proposed scheme, case study is presented for an electrical relay. The result is compared with that obtained by the expert system