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Topology design optimization offers tremendous opportunity in design andmanufacturing freedoms by designing and producing a part from the ground-upwithout a meaningful initial design as required by conventional shape designoptimization approaches. Ideally, with adequate problem statements, toformulate and solve the topology design problem using a standard topologyoptimization process, such as SIMP (Simplified Isotropic Material withPenalization) is possible. In reality, an estimated over thousands of designiterations is often required for just a few design variables, the conventionaloptimization approach is in general impractical or computationally unachievablefor real world applications significantly diluting the development of thetopology optimization technology. There is, therefore, a need for a differentapproach that will be able to optimize the initial design topology effectivelyand rapidly. Therefore, this work presents a new topology design procedure togenerate optimal structures using an integrated Generative Adversarial Networks(GANs) and convolutional neural network architecture.

A novel topology design approach using an integrated deep learning network architecture