Lightweight design optimization of truss structure using long short-term memory network

To satisfy the requirements of the unmanned vehicle such as high reliability inmancuvcrability and low development costs, the idea of lightweight structure was involved in the early design process of the truss structure design optimization. To realize the lightweight body structure design, a Long Short-Term Memory Network based Design Optimization (LSTM-DO) method was proposed for solving the slow convergence speed and local optimization problems with its fast-searching and high-precision optimization abilities. A parameterized model and a Finite Element Analysis(¥KA) model of the unmanned vehicle truss structure were constructed, which used surrogate model to accelerate the evaluation speed of the car body structure, and realized the rapid and accurate truss structure program generation by combining with the LSTM-DO method. Compared to the commonly used Gradient-Based Algorithms(GBA) and Evolutionary Algorithms(EA), the proposed LSTM-DO method offered significant advantages in terms of solution performance, convergence speed and robustness.