Multi-Parameter Structural Topology Optimization Method Based On Deep Learning

Abstract:

The traditional topology optimization method based on finite element method requires multiple finite element calculation and iterations， which consumes a lot of computational resources and time. In order to improve the efficiency of topology optimization， the paper takes topology optimization of cantilever beam as an example and proposes a generative convolutional neural network （CNN） model based on residual connections， which considers four optimization parameters： filter radius， volume fraction， loading point and loading direction. And the influence of different loss functions and number of samples on the accuracy of generative CNN model is discussed at length. The results show that the proposed model has high accuracy and generalization ability， and the mean structural similarity index between the model prediction and finite element method can reach 0.9720， the mean absolute error is 0.0143. And the prediction time of the model is only 0.0041 of finite element method， which significantly improves the efficiency of topology optimization.

Key words:topology optimization;convolutional neural networks;solid isotropic material with penalization;structural similarity index