Research on case retrieval strategy based on drawing die of automobile coverings

Abstract

Abstract To improve the accuracy and efficiency of case retrieval in the process of intelligent design of automobile panel drawing die, this paper proposes a case retrieval strategy based on model reasoning and an improved k-nearest neighbor (KNN) method. Through the functional representation and modular description of the existing knowledge of automobile panel drawing die design, the problems to be solved are compared with the model, and the preliminary screening is completed. Then the nearest neighbor algorithm is used to realize the retrieval, and the subjective and objective weight assignment method is used to optimize the retrieval strategy. The subjective weight uses the improved three-scale analytic hierarchy process to avoid the subjectivity increase caused by the judgment matrix’s artificial adjustment. The objective weight uses the grey wolf optimization algorithm, and the dynamic adaptive calculation of the average similarity is designed to enhance the reliability of the fitness function. Finally, the case retrieval of the drawing die during the design of a certain type of panel die is taken as an example to test. The retrieval strategy can accurately complete the retrieval of historically similar cases, which verifies the effectiveness of the proposed instance retrieval strategy.