Robust topology optimization of structures subjected to random loads using the Fourier-TOuNN
To promote the engineering application of topology optimization,it is imperative to consider the randomness of structural loads often encountered in practical scenarios.In this paper,an efficient framework for robust structural topology optimization based on the neural network is developed,which updates the density variable describing the structural topology by optimizing the weights of the Fourier-TOuNN neural network.In the developed framework,the weighted sum of the mean and standard deviation of the structural compliance under random loads is introduced as the objective function,thereby defining the structural robustness under random loads.Using the automatic reverse differentiation function of the neural network,the automatic derivation of the sensitivities in the optimization process can be realized.With the controllablility of local of the Fourier-TOuNN,small auxiliary components can be generated in the structure to withstand random loads.Numerical examples show that robust structural designs can be efficiently obtained by using the developed framework.
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