Abstract It has been a vital issue to ensure both the accuracy and efficiency of computational models for analyzing the ballistic impact response of fiber-reinforced composite plates (FRCP). In this paper, a machine learning (ML) model is established in an effort to bridge the ballistic impact protective performance and the characteristics of microstructure for unidirectional FRCP (UD-FRCP), where the microstructure of the UD-FRCP is characterized by the two-point correlation function. The results showed that the ML model, after trained by 175 cases, could reasonably predict the ballistic impact energy absorption of the UD-FRCP with a maximum error of 13%, indicating that the model can ensure both computational accuracy and efficiency. Besides, the model’s critical parameter sensitivities are investigated, and three typical ML algorithms are analyzed, showing that the gradient boosting regression algorithm has the highest accuracy among these algorithms for the ballistic impact problem of UD-FRCP. The study proposes an effective solution for the traditional difficulty of the ballistic impact simulation of composites with both high efficiency and accuracy.
High dynamic strength is of fundamental importance for fibrous materials that are used in high-strain rate environments. Carbon nanotube fibers are one of the most promising candidates. Using a strategy to optimize hierarchical structures, we fabricated carbon nanotube fibers with a dynamic strength of 14 gigapascals (GPa) and excellent energy absorption. The dynamic performance of the fibers is attributed to the simultaneous breakage of individual nanotubes and delocalization of impact energy that occurs during the high-strain rate loading process; these behaviors are due to improvements in interfacial interactions, nanotube alignment, and densification therein. This work presents an effective strategy to utilize the strength of individual carbon nanotubes at the macroscale and provides fresh mechanism insights.
Synthetic high-performance fibers present excellent mechanical properties and promising applications in the impact protection field. However, fabricating fibers with high strength and high toughness is challenging due to their intrinsic conflicts. Herein, we report a simultaneous improvement in strength, toughness, and modulus of heterocyclic aramid fibers by 26%, 66%, and 13%, respectively, via polymerizing a small amount (0.05 wt%) of short aminated single-walled carbon nanotubes (SWNTs), achieving a tensile strength of 6.44 ± 0.11 GPa, a toughness of 184.0 ± 11.4 MJ m-3, and a Young's modulus of 141.7 ± 4.0 GPa. Mechanism analyses reveal that short aminated SWNTs improve the crystallinity and orientation degree by affecting the structures of heterocyclic aramid chains around SWNTs, and in situ polymerization increases the interfacial interaction therein to promote stress transfer and suppress strain localization. These two effects account for the simultaneous improvement in strength and toughness.
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