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박용길(Yong K.Park),조형석(Hyung S. Cho) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
In this paper a new active assembly algorithm for chamferless precision parts mating, is considered. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as imperfect knowledge of the parts being assembled as well as the limitation of the devices performing the assembly. To cope with these problems, a self-learning rule-based assembly algorithm is proposed by integrating fuzzy set theory and neural network. In this algorithm, fuzzy set theory copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly algorithm algorithm is evaluated through a series of experiments. The results show that the self-learning fuzzy assembly scheme can be effectively applied to chamferless precision parts mating.