Functionally graded foam materials for head impact protection

Head injuries commonly result from impact accidents, underscoring the urgent requirement for enhanced helmet protection. Functionally graded foam (FGF) materials present promising avenues for augmenting the design of impact-attenuating liners in helmets, thereby mitigating head injuries. This study aims to investigate the performance of the FGF materials in protecting against head impact and provide an effective optimization methodology to support the efficient and reliable design of the FGF materials. The foam finite element model was developed and coupled with a highly biofidelic head model to simulate the FGF-head frontal impact scenarios. A parametric study based on the coupled FGF-head model was conducted to explore the effects of the configuration parameters on the protection performance of the FGF materials. The study demonstrated that the negative-gradient FGF materials provide better head impact protection than the uniform density foam (UDF) and positive-gradient FGF materials, despite the potential disadvantage in energy absorption. A discrete optimization method based on the FGF-head model was developed and illustrated to efficiently achieve an optimized design for FGF protectability, thereby reducing the risk of head injuries. The optimized FGF design reduced the Head Injury Criterion (HIC) value by 40.4 % compared to the conventional UDF design, from 1660 to 990. Therefore, this study provides a further understanding and a new optimization insight into designing FGF materials to improve head impact protection.