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Influence of surface preparation and polymer backing properties on the quasi-static and impact response of ceramic faced 1D armour systems

材料科学 复合材料 聚氨酯 盔甲 聚合物 陶瓷 弹道冲击 聚碳酸酯 胶粘剂 梁(结构) 复合数 表面能 结构工程 图层(电子) 工程类
作者
Tom Commins,Aaron Graham,Clive R. Siviour
出处
期刊:International Journal of Impact Engineering [Elsevier]
卷期号:180: 104708-104708 被引量:3
标识
DOI:10.1016/j.ijimpeng.2023.104708
摘要

Ballistic impact is a highly complex environment, the understanding of which is compounded by the advanced materials used for ceramic-polymer composite armours. In this study, the influence of surface preparation and polymer backing properties on the energy absorptive capabilities and ballistic performance of simplified model armours was approached via a methodical investigation using simplified materials and geometries. Quasi-1D (beam) specimens consisting of polyurethane-backed alumina were loaded under three-point bend quasi-statically and at impact speeds. The specimen geometries allowed for the fracture pathways to be observed. To produce the specimens, two polyurethanes with different glass transition temperatures were cured onto alumina strips, eliminating the requirement for a dedicated adhesive and thus simplifying the interfacial dynamics. Further modifications were made by applying a primer surface treatment, by using unbonded polyurethane and adhered polyurethane, and by replacing the polyurethane with glass-reinforced polycarbonate. The beam specimens were loaded quasi-statically at 0.05 mm s−1 at ambient and sub-ambient temperatures, and at 50-300 m s−1 at ambient temperatures. The quasi-static specimens were found to fail by one of two failure modes with approximately a 3x energy absorption difference; the failure was highly dependent upon the interface and polymer characteristics. Under impact, a significant proportion of the energy absorption was provided by the kinetic energy of the fragments. Although, the polymer backing and interfacial properties were found to influence fracture paths during loading. FEA simulations were used to model the behaviours using characterisation data from a previous study; these were found to predict the experimental response well.

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