An intelligent assembly algorithm for chamferless parts mating in an in-line continuous transfer system

This paper describes an intelligent assembly algorithm for part mating in an in-line continuous transfer system by integrating neural network and fuzzy set theory. The fuzzy set theory copes with the imprecision and vagueness associated with actual assembly task, while the neural net theory copes with limitation of fixed fuzzy rules by learning. The assembly performance of the intelligent algorithm is evaluated through a series of experiments. Experimental results show that the method can be an effective method for assembly in an in-line continuous transfer system.