Simulation-informed and data-driven design of bionic–TPMS composite structures via additive manufacturing

High-temperature robots face dual challenges of thermal protection and load-bearing under extreme conditions. Traditional thermo-mechanical coupled structures, due to their single-function design, fail to meet the demands of lightweighting and multi-functional integration. Bionic structures and triply periodic minimal surface (TPMS) structures each exhibit excellent mechanical and thermal performance, rendering them promising solutions to this challenge. However, integrate simulation-informed and data-driven methods to enable collaborative design of these two types of structures and provide precise guidance for configuration optimization remains a critical scientific challenge for their reliable application. This study proposes a simulation-informed and data-driven collaborative optimization method that combines deep learning and physical simulation to construct a predictive model, and efficiently establishing a mapping relationship between structural parameters and performance responses. Simulation and experimental results show that the optimised structure achieves a 14.25% improvement in thermal shielding capacity, and a 44.85% increase in load-bearing capacity, significantly verifying the effectiveness of the proposed method. The proposed bionic–TPMS composite structure exhibits excellent thermo-mechanical coupled performance and holds promise for application in thermo-mechanical system design, offering new insights and theoretical support for the engineering application of high-temperature robots in extreme environments.