Optimization of soft armor: the response of single-ply para-aramid and ultra-high molecular weight polyethylene fabrics under ballistic impact
Source abstract
Abstract
Typical soft armor systems are constructed of multiple layers of a single fabric type. This empirical research sought to begin optimization of these systems through hybridization, sequencing dissimilar armor fabrics to maximize their ballistic protective performance, by first investigating single plies with a spectrum of properties to determine their behavior and response to impact. Eight individual plain weave fabrics with varying yarns and thread counts were manufactured from para-aramid and ultra-high molecular weight polyethylene (UHMWPE) yarns and physical and ballistic characterizations were conducted. The ballistic impact tests established the specific energy absorption (SEA) of each fabric across a range of impact velocities (340–620 m·s –1 ) and the transverse displacement wave velocity across the rear of the fabric was found using digital image correlation. Low cover factor ( C fab ) fabrics (0.74–0.84) consistently showed faster transverse wave speed than the high C fab fabrics (0.84–0.96) for any given yarn type. The relative SEA of the fabrics varied dependent on both the impact velocity and number of plies impacted. It was found that lower C fab fabrics had the highest SEA, critical velocity and transverse wave velocity. UHMWPE fabrics were not considered suitable for a woven hybrid system as they had a significantly lower SEA compared to all the para-aramid fabrics. Results indicate that a hybrid system, when considered as a theoretical spaced system, would benefit from higher C fab fabrics as rearward layers. However, transverse wave results suggest the lower response of these fabrics may inhibit lower C fab fabrics at the front of a combined hybridized system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mechanical Behavior of Composites · Occupational Health and Performance · Textile materials and evaluations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University of Ulster at Jordanstown, UK
Funding
Department for Employment and Learning, Northern Ireland · Defence Science and Technology Laboratory
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