Intelligent Optimization Method and Rapid Design of Cross-scale Structure
The multiscale topology optimization design has greatly stimulated the lightweight potential of the structure and play an important role in the development of advanced equipment.However,the structural topology optimization algorithm based on traditional finite element is difficult to meet the needs of rapid product iteration.To this end,this paper proposes a coupled deep learning-based cross-scale topology optimization method to establish a deep learning model for fast generation of dual-scale topologies by integrating residual neural networks(Resnet),U-net architecture and attention mechanism in SEnet.The training data are generated using a bidirectional evolutionary structure optimization algorithm,and the model is tested with a completely new set of data.Numerical examples show that the proposed deep learning model can efficiently and accurately generate macroscopic material distribution and microscopic topology based on various boundaries.
multiscale topology optimizationcoupled deep learningmicro structurelight weigh
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