Integrating Geometric Data into Topology Optimization via Neural Style Transfer.

ABSTRACT: This research proposes a novel topology optimization method using neural style transfer to simultaneously optimize both structural performance for a given loading condition and geometric similarity for a reference design. For the neural style transfer, the convolutional layers of a pre-trained neural network extract and quantify characteristic features from the reference and input designs for optimization. The optimization analysis is evaluated as a single weighted objective function with the ability for the user to control the influence of the neural style transfer with the structural performance. As seen in architecture and consumer-facing products, the visual appeal of a design contributes to its overall value along with mechanical performance metrics. Using this method, a designer allo

SUBMITTER: Vulimiri PS 

PROVIDER: S-EPMC8400862 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature