Construction and application of data-driven knowledge adjacency network for product CMF design

Abstract

To solve the problems of over-reliance on personal experience, lack of thinking about users' perceptual preferences, and neglect of the overall coupling characteristics of CMF design, the construction and application methods of product CMF design knowledge adjacency network are proposed based on the multi-layer network, intuitionistic fuzzy set, and gray correlation method theory. Firstly, the construction of the product CMF design multilayer network is completed by extracting and selecting the design resources of product families with similar imagery. Secondly, considering the product CMF design characteristics, a multi-layer network association division method based on leader nodes is proposed, and the construction of the CMF design knowledge adjacency network is further completed. Thirdly, based on intuitionistic fuzzy set theory, user preferences are considered to further improve the science of evaluation data. Finally, the calculation of association coefficients is combined with the gray correlation method, and the optimal matching between the adjacency network and the product surface assignment solution space is completed to assist designers to improve the design efficiency. The proposed method is applied to the CMF design work of a smart tractor, and perceptual experiments are set up to evaluate the design results, and the results show that this design meets the target requirements, which proves the usefulness of the method to assist the designer's work.