A dynamic knowledge graph-based assembly sequence planning method

In assembly sequence planning (ASP) of complex products, hierarchical structure, geometric feasibility, assembly tool changes, and assembly direction changes should be fully considered. Since the traditional information models of ASP are mostly static and abstract matrices, the outdated information reduces sequence rationality and increases time costs. To address these limitations, a novel decision-making framework based on dynamic knowledge graph (DKG) is proposed to build an intuitive semantic information model, planning the assembly sequences of complex products. An automated DKG generation method with updating mechanisms is designed to ensure that the DKG remains effective throughout the construction and maintenance process. A double-layer degree ordering algorithm (DDOA) is proposed to analyze sequence constraints within the DKG for obtaining a higher-quality assembly sequence. Comparative experiments demonstrated that the DDOA exhibits optimal performance. The solved assembly sequence possesses the best value of the objective function, with the fewest changes in assembly directions and assembly tools, as well as the shortest runtime.