GNN-based reverse design for mechanical systems: Bridging trajectory and mechanical design

In today's rapidly evolving landscape, conventional construction techniques like masonry and concrete pouring are falling short in meeting the evolving needs of contemporary designs that demand versatile and adaptable mechanical solutions. However, the established methods for crafting such mechanisms are time-intensive and frequently fall short in achieving the desired trajectory outcomes. In light of this challenge, this paper takes up the mantle of presenting a streamlined and accurate approach for devising trajectory-mechanism designs. This is achieved through the synergistic integration of multiple deep-learning prediction models, which serve to unveil the intricate interplay between the components of a linkage mechanism and the intended trajectory. To establish a robust foundation, a ground truth generation system is crafted using Rhino 3D modeling software. This system lays the groundwork for producing essential data to be harnessed in the training and testing of the models. A comprehensive series of experiments is then conducted to unearth solutions that can generate predictive trajectories aligned with stringent design requisites. The efficacy of the proposed framework is tested on the intricate StrandBeest structure to gauge its adaptability. The ensuing quantitative and qualitative analyses offer both empirical evidence and valuable insights into the potency of the proposed methodology. In short, the proposed method introduces an innovative paradigm for fashioning trajectory-mechanism designs in a more resourceful and swift manner when juxtaposed with prior methodologies. This endeavor not only addresses current limitations but also represents a step forward in ushering efficiency and effectiveness into this realm of research and application.