Structural Synthesis of Epicyclic Gear Trains by Deep Learning and Generative AI for Adaptive Automation

Structural synthesis of Epicyclic Gear Trains (EGTs) is a computationally demanding activity, particularly when identifying isomorphism between complex topologies and producing new gear designs to support high-performance automation systems. Graph-theoretic and algebraic methods are traditional and involve manual intervention and duplicate solutions. To address this shortcoming, this paper proposes a DL-based Generative AI framework for the automated synthesis and classification of EGTs. A Generative Adversarial Network (GAN) is trained on existing EGT topologies, learning their structures, creating new feasible structural mechanisms, and identifying duplication through degree sequence estimation and graph matching. The strategy is combined with the calculation of the connectivity matrix and the representation of the structural graph to ensure manufacturability and kinematic feasibility. The effectiveness of the proposed AI model is validated by the analysis of different EGTs, with 4--5 links, single DOF. Findings demonstrate that the GAN-based synthesis reliably distinguishes structurally distinct gear trains, eliminates pseudo-isomorphic designs, and saves a significant amount of design time. The technique justifies adaptive automation by designing intelligent mechanisms that require minimal human intervention. The paper demonstrates that AI-based synthesis can be highly effective in next-generation smart factories, robotic actuation, transmission systems, and reconfigurable automation platforms.