A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

Abstract

In the assembly sequence planning (ASP) of aviation products, recalibration of components or sufficient space to assemble subsequent components are critical factors for ensuring product quality. To address this need, a fine-grained ASP (FASP) is defined to take assembly operations as units to plan sequences. Lots of operations have complex sequence constraints that are attended unequally in the FASP. A method based on knowledge graph (KG) and deep reinforcement learning is proposed to plan assembly operations. Firstly, continuous and discrete procedures are defined, and a quantitative characterization method is presented to deduce complex constraints objectively. Then, a dynamic KG is designed to establish and update the information model mainly composed of constraints. Finally, a labeled degree centrality algorithm (LDCA) considers edge labels to minimize the number of assembly tool changes and assembly direction changes for sequences. An improved deep Q-network (IDQN) introduces a convolutional layer to extract local features of technical requirements for planning procedures more efficiently. A helicopter structure assembly is used to verify the effectiveness of the proposed method. The improved algorithms have better performance in solving speed, sequence quality, and convergence speed than ordinary ASP methods, respectively. The fine-grained assembly sequence is more reasonable and feasible by comparing it with the ordinary sequence.