Evaluating the Performance of 3D-Printed Stab-Resistant Body Armor Using the Taguchi Method and Artificial Neural Networks.

Abstract

Additive manufacturing has promising potential for the development of 3D-printed protective structures such as stab-resistant body armor. However, no research to date has examined the impact of 3D printing parameters on the protective performance of such 3D-printed structures manufactured using fused filament fabrication technology. This study, therefore, investigates the effects of five key printing parameters: layer thickness, print speed, print temperature, infill density (Id), and layer width, on the mechanical and protective performance of 3D-printed polycarbonate (PC) armor. A Taguchi L₂₇ matrix was employed to systematically analyze these parameters, with toughness, stab penetration depth, and armor panel weight as the primary responses. ANOVA results, along with the Taguchi approach, demonstrated that Id was the most influential factor across all print parameters. This is because a higher Id led to denser structures, reduced voids and porosities, and enhanced energy absorption, significantly increasing toughness while reducing penetration depth. Morphological analysis supported the statistical findings regarding the role of Id on the performance of such structures. With optimized printing parameters, no penetration to the armor panels was recorded, outperforming the UK body armor standard of a maximum permitted knife penetration depth of 8 mm. Moreover, an artificial neural network (ANN) utilizing the 5-14-12-3 topology was created to predict the toughness, stab penetration depth, and armor panel weight of 3D-printed armors. The ANN model demonstrated better prediction performance for stab penetration depth compared to the Taguchi method, confirming the successful application of such an approach. These findings provide a critical foundation for the development of high-performance 3D-printed protective structures.