Abstract
Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through the offline training, the mapping relationship between initial design conditions and topology optimization results is obtained. From this mapping, we estimate the optimal topologies for novel loading configurations. Numerical examples display that the online prediction results are consistent with the results of the topology optimization method. Furthermore, the network parameters are calibrated, and accurate structure prediction is achieved based on the algorithm. In addition, this method ensures the accuracy of high-resolution structural prediction on the premise of small samples.