An assembly sequence is considered to be optimal when it minimizes assembly cost while satisfying assembly constraints. The assembly cost relates to assembly operations, assembly motions, and assembly direction changes. This study proposes a simulated-annealing method for the generation of such assembly sequences in robotic assembly. This method reflects the assembly cost to an energy function associated with the assembly sequence. The energy function is iteratively minimized and occasionally perturbed by a simulated annealing until no further change in the energy occurs. As a result, an assembly sequence with a low assembly cost is finally found. To show the effectiveness of the proposed method, case study is presented for an electrical relay. The performance is evaluated from the comparison of the result with the ones obtained from an expert system and a neural network-based approach, previously presented methods.

This paper proposes a mathematical approach to the analysis of disassemblability of a product for generating stable robotic assembly sequences. The disassemblability of a part, which represents a cost for disassembling the part from a base assembly, is derived in terms of both separability of a part from a base assembly and motion instability of the base assembly. Then, stable robotic assembly sequences are found in the list of parts, each of which is sequentially chosen to have a locally maximum disassemblability. To show the effectiveness of the proposed method, case studies are presented for industrial products. The results are compared with those of previous researches. The comparison shows that the proposed method can effectively generate stable robotic assembly sequences and provide essential informations on fixture design.

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.