A surrogate model-driven assembly coordination framework for aircraft components based on cooperative multi-agent deep reinforcement learning

This study presents a multi-agent reinforcement learning (MARL) approach to address coordination challenges in aircraft component assembly. A machine learning–based surrogate model is developed to approximate component deformation, enabling the construction of a realistic and computationally efficient MARL training environment. Within this environment, multiple agents rapidly learn strategies to optimize both individual component deformations and inter-component coordination. The surrogate model compresses the high-dimensional displacement fields into lower-dimensional representations, significantly reducing the complexity of the state space. The reward function combines both local and coordination rewards, where the local reward evaluates manufacturing accuracy at the component level, and the coordination reward assesses alignment accuracy between components. By exchanging local state information during training, agents enhance cooperation, accelerate convergence, and improve overall assembly performance. The effectiveness of the proposed method is demonstrated through a fuselage panel assembly case study, achieving average reductions of 94.91 % in panel deformation and 95.02 % in inter-panel gaps. This framework offers a promising solution for coordinating deformable structures, substantially enhancing both assembly quality and efficiency.