Real-time topology optimization based on deep learning for moving morphable components

The moving morphable component (MMC) method, an important engineering structural optimization algorithm, achieves a boundary evolution through the migration and superposition of a series of moving morphable display components, resulting in structural optimization. Instead of the original linear skeleton thickness quadratic variation component, an elliptical initial component is used for topology optimization, which reduces the number of design variables of the initial component itself to achieve the purpose of reducing the computational time cost. As the number of initial components increases, the intermediate iterative computation becomes increasingly time-consuming; the initial components of the different parameters generate significant differences in the topological configuration. To obtain an optimal topology in an accurate and real-time manner, a Pyramid Attention U-Net (PA-U-Net) deep learning model is proposed to improve the optimization design and to avoid intermediate iterative computations. The dataset is generated by an improved MMC method for training. According to the results, the method not only obtains the optimal topology of the initial components under various parameter conditions in an accurate and timely manner and in a negligible amount of computing time, but it also achieves an accuracy of 90.89%, which is superior to other deep learning models. The organic combination of deep learning and topology optimization has many prospects in the design optimization of large engineering structures.

Automated summary

The moving morphable component (MMC) method is an important engineering structural optimization algorithm that achieves structural optimization through the migration and superposition of a series of moving morphable display components. In this research, a Pyramid Attention U-Net (PA-U-Net) deep learning model is proposed to improve the optimization design and avoid intermediate iterative computations. Experimental results show that the method has high accuracy and low computational time cost, and it shows potential for application in the design optimization of large engineering structures.