Design of Additively Manufactured Functionally Graded Cellular Structures

Functionally graded cellular structures usually exhibit excellent energy absorption properties, showing great promise to potentially replace conventional uniform cellular structures for next-generation passive protection systems. Meanwhile, Additive Manufacturing (AM) technologies have enabled the rapid high-resolution prototyping of functionally graded gyroid-based cellular structures. Due to the complexity of functionally graded gyroid-based cellular structures, it necessitates an efficient computational method with minimal computational cost to find the optimal design for energy absorption. This paper presents a physics-informed machine learning method for designing functionally graded gyroid-based cellular structures that can be additively manufactured to achieve an optimized energy absorption performance. We develop physics-based simulations for different functionally graded gyroid-based cellular structures. Then we use these simulation results to build a machine learning surrogate model. The adaptive sampling method is adopted to improve the fidelity of the surrogate model. This proposed method provides a computational efficient approach for the optimal design of functionally graded gyroid-based cellular structures, which can achieve the maximum energy absorption at a specific average volume fraction for passive protection systems, considering manufacturing restrictions. Using the proposed method with minimal computational cost, the optimal design of the passive protection system can be obtained, and the optimized functionally graded gyroid-based cellular structures are additively manufactured and tested for the design validation studies.