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 fuselage 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 employs
the Dueling Double Deep Q-Learning (D3QN) algorithm to refine the
decision-making capabilities of sequential actuator placements. The environment
is meticulously crafted to enable sequential and incremental selection of an
actuator based on system states. We formulate the actuator selection problem as
a submodular function optimization problem, where the sub-modularity properties
can be adopted to efficiently achieve near-optimal solutions. 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.

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