A SELF-LEARNING RULE-BASED CONTROL ALGORITHM FOR CHAMFERLESS PART MATING

In this paper a new active assembly algorithm is proposed for chamferless precision parts mating. The motivation of the development is based upon the observation that present assembly tasks require an extremely high positional accuracy and a good knowledge of mating parts. However, use of conventional assembly methods 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 limitations of the devices performing the assembly. To cope with these problems, a self-learning rule-based control algorithm for precision assembly is proposed by integrating fuzzy set theory and neural networks. In this algorithm, fuzzy set theory copes with the complexity and the uncertainties of the assembly process, while a 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 control algorithm is evaluated through a series of experiments. The results show that the self-learning fuzzy control scheme can be effectively applied to chamferless precision part mating.