SAPO-RL: Sequential actuator placement optimization for fuselage assembly via reinforcement learning

Precise assembly of composite fuselages is critical for aircraft assembly to meet the ultra-high precision requirements. Due to dimensional variations, there is a gap when two fuselages assemble. In practice, actuators are required to adjust fuselage dimensions by applying forces to specific points on fuselage edge through pulling or pushing force actions. The positioning and force settings of these actuators significantly influence the efficiency of the shape adjustments. The current literature usually predetermines the fixed number of actuators, which is not optimal in terms of overall quality and corresponding actuator costs. However, optimal placement of actuators in terms of both locations and number is challenging due to compliant structures, complex material properties, and dimensional variabilities of incoming fuselages. To address these challenges, this paper introduces a reinforcement learning (RL) framework that enables sequential decision-making for actuator placement selection and optimal force computation. Specifically, our methodology first innovatively formulates the actuator placement problem as a submodular problem, where the sub-modularity properties can be adopted to efficiently achieve near-optimal solutions. Then we employ the Dueling Double Deep Q-Learning (D3QN) algorithm to refine the decision-making capabilities of sequential actuator placements via our designed orthogonal space. The proposed methodology has been comprehensively evaluated through numerical studies and comparison studies, demonstrating its effectiveness and outstanding performance in enhancing assembly precision with limited actuator numbers.