VoroTO: Multiscale Topology Optimization of Voronoi Structures using Surrogate Neural Networks
<|reference_start|>VoroTO: Multiscale Topology Optimization of Voronoi Structures using Surrogate Neural Networks: Cellular structures found in nature exhibit remarkable properties such as high strength, high energy absorption, excellent thermal/acoustic insulation, and fluid transfusion. Many of these structures are Voronoi-like; therefore researchers have proposed Voronoi multi-scale designs for a wide variety of engineering applications. However, designing such structures can be computationally prohibitive due to the multi-scale nature of the underlying analysis and optimization. In this work, we propose the use of a neural network (NN) to carry out efficient topology optimization (TO) of multi-scale Voronoi structures. The NN is first trained using Voronoi parameters (cell site locations, thickness, orientation, and anisotropy) to predict the homogenized constitutive properties. This network is then integrated into a conventional TO framework to minimize structural compliance subject to a volume constraint. Special considerations are given for ensuring positive definiteness of the constitutive matrix and promoting macroscale connectivity. Several numerical examples are provided to showcase the proposed method.<|reference_end|>
@article{padhy2024voroto:,
    title={VoroTO: Multiscale Topology Optimization of Voronoi Structures using
  Surrogate Neural Networks},
    author={Rahul Kumar Padhy, Krishnan Suresh, Aaditya Chandrasekhar},
    journal={arXiv preprint arXiv:2404.18300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18300},
    primaryClass={cs.CE cs.NA math.NA}
}