Experimental Methodology Using Digital Image Correlation to Assess Ballistic Helmet Blunt Trauma

As modern helmets have become quite capable of defeating the penetration capabilities of ballistic threats, Soldiers may experience head injuries due to blunt trauma caused by helmet back face deformation (BFD). Possible resulting injuries include skull fracture, hematoma, concussion, contusion, diffuse axonal injury, etc. Some of these injuries have been associated with traumatic brain injury. In order to assess potential injury mechanisms prior to fielding new helmets, we have developed a means to experimentally replicate and measure helmet BFD that can be correlated to injury criteria. In this study, helmet performance test methodology is developed using a digital image correlation (DIC) technique. DIC provides the capability to measure dynamic displacements, thereby providing the ability to calculate deformation, velocity, and acceleration rates. We have shown that digital image correlation is an experimentation technique that accurately captures BFD area and rate of deformation for impacts against combat helmets. We used the DIC data to calculate a new metric; the available energy that could potentially impact a Soldier’s head. Our study shows that DIC data upholds the hypothesis that helmet BFD mechanically loads the skull similar to a direct impact from a less-than-lethal projectile or blunt object impact. The available energy obtained from DIC measurements was used to calculate the blunt criterion (BC) for helmet standoff distances of 12.7 mm (0.5 in) and 19.1 mm (0.75 in), which in turn can provide a prediction of the probability of abbreviated injury scale (AIS) levels and, in particular, skull fracture. DIC can be used to provide dynamic helmet performance data that will allow increased understanding of BFD and quantitative assessment and validation of helmet performance results. Knowledge of the conditions leading to head trauma obtained through DIC experimentation should enable the selection of new energy-absorbing materials for helmets; thus, allowing new helmet design candidate performances to be objectively evaluated. Test data and characterization of helmet response could then be used to achieve improved warfighter survivability.