Reliability-based topology optimization collaborated with deep learning
Structural topology optimization obtains the best layout of materials in the design domain with optimal performance under given constraints.Two challenges faced by the traditional topology optimization include:(1)the effects of parameter uncertainties are not quantitatively accounted for,resulting in optimized structures that often do not satisfy performance constraints in service;(2)the resolution of topology optimization based on finite element models is heavily dependent on the finite element mesh.In this paper,the deterministic transformation of reliability-based topology optimization(RBTO)model is realized based on sequential optimization and reliability assessment method by quantitatively accounting for the inherent uncertainty with stochastic parameters.Further,based on the process similarity between the neural network training and the topology optimization,the network parameters(including weights and bias)are mapped to the relative density of all elements by a deep neural network.As a result,the topology optimization process is converted into a network training process by constructing a loss function that accounts for reliability.Finally,it is demonstrated that the proposed method can achieve higher resolution of the optimized structure while meeting the reliability requirement.
topology optimizationreliability-based optimizationdeep neural networkfinite element analysis
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