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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]