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In the present research, glass and basalt fiber-reinforced polypropylene composites' behaviour in quasi-static and dynamic conditions is studied. Composites were fabricated by vacuum assisted Compression molding method. Composites failure under quasi-static tension and compressive conditions was studied along with its failure behaviour under low-velocity impact and super-sonic shock loading under dynamic conditions. The study results showed that basalt fiber-reinforced polypropylene (Basalt/PP) composite's tensile and compressive strength is higher than glass fiber-reinforced polypropylene (Glass/PP). The Basalt/PP showed no penetration against low velocity impact (LVI) with negligible deformations till 50 J. However, the Glass/PP perforated at 50 J with various failure patterns occurring at back side. The fiber-matrix interface adhesion plays an important role in super-sonic shock loading by absorbing shock wave energy due to ductile nature of polypropylene and the two composites absorbed energy via matrix and fibers failure, no brittle failure of laminates occurred under shock loading. [ABSTRACT FROM AUTHOR]

Despite its relevance for law enforcement applications, the design of soft armor has mainly been based on a trial-and-error approach. In this paper, a combined experimental, machine learning, and finite element analysis framework is used to build a predictive numerical model for the analysis and hence, design of soft armor. The material models for major components of the soft armor certification system--bullet, shoot pack, straps, and clay backing, are first constructed using laboratory tests and publicly available data. Next, three metrics, namely, back face signature (BFS), number of penetrated shoot-pack layers, and mushrooming of the bullet are established to gauge the model's accuracy with respect to the laboratory ballistic test data. A machine learning (ML) model is used as a surrogate to predict the BFS and the number of eroded elements. Finally, optimized material model parameters are obtained through ML-based surrogate model and Bayesian optimization algorithm. The final validation of the developed framework is carried out using laboratory ballistic test data involving multiple shots on the shoot pack. The results indicate that reliable predictive data can be obtained using the developed process, and likely, can be extended for use in modeling other impact simulations. [ABSTRACT FROM AUTHOR]

This paper presented an experimental and numerical investigation on the low velocity impact response of thermoplastic hybrid composites reinforced with Kevlar/basalt fabrics. Two hybrid and one Kevlar homogeneous composite laminates were manufactured with polypropylene as a resin. In the hybrid composites, one hybrid composite (H-1) was manufactured with alternate stacking of four layers of basalt and four layers of Kevlar and the second hybrid composite (H-2) was manufactured with four Kevlar layers on front face and four basalt layers on back face. Low velocity impact tests were performed using a drop-weight impact equipment at three different energies (25J, 50J and 75J). Among the two hybrid composites H-1 hybrid composite exhibited 15.58-20.79% and 13.47-20.47% improvement in the peak force and energy absorption, respectively, than the H-2 hybrid composite. The peak force and energy absorption of Kevlar homogeneous composite was also improved by 10.07-14.37% and 5.38-11.29%, respectively, due to hybridization. A three dimensional (3D) dynamic finite element software, Abaqus/Explicit, was implemented to simulate the experimental results of low velocity impact tests. A user-defined material subroutine (VUMAT) based on Chang-Chang linear-orthotropic damage model was implemented into the finite element code. The predictions from numerical simulation were found to be in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]