Identification of Key Factors and Mining of Association Relations in Complex Product Assembly Process

There are many factors involved in the assembly process of complex products, but only a few of the key influencing factors really affect the performance of the product assembly. In order to ensure the performance of complex products, it is necessary to model the deviation transfer flow of the assembly process of complex products and identify key processes and characteristics and determine its scope of influence. This paper establishes the set of assembly performance influencing factors by analyzing the complex product assembly process and uses complex networks combined with entropy weight method to identify the influencing factors of product performance to achieve the identification of key influencing factors. Based on the key factor identification results using an Apriori algorithm to mine the association rules between key factors and performance to provide guidance for the assembly of complex products and achieve transparent and controllable performance. The assembly process of a certain type of aeroengine low-pressure fan rotor is verified as the research object. The results show that the proposed method can identify the key factors in the multistage assembly process and establish an association rule library to provide decision support for process adjustment.