Search

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.

Supplemental material for Building a predictive soft armor finite element model combining experiments, simulations, and machine learning by Tanu Pittie, Kartikeya Kartikeya, Naresh Bhatnagar, NMA Krishnan, Thilak Senthil and Subramaniam D Rajan in Journal of Composite Materials

Adhesives are viscoelastic polymeric materials with rate-dependent behaviour. A personal body armour for higher threat perception consists of hard ceramic facing adhesively joined to a comp...

High temperature blast and ballistic resistant light weight fibre reinforced plastic composites are very much in demand. Accordingly, the present investigation is focused on the quasi-static and high strain rate behaviour of Basalt/Polyetherimide (PEI) composite. Nine different types of composite samples were fabricated by compression moulding. The moulding pressures and temperatures were varied in the range of 15–25 bars and 325 °C−375 °C, respectively. Quasi-static testing was done at the rate of 3 mm/min to establish the optimum moulding parameters. High strain rate compression tests in the strain rate range of 900/s−3200/s were performed on the optimum moulded composite specimens. High strain rate testing revealed that 40 layered Basalt/PEI composite moulded at 25 bar pressure and 350 °C attained highest stress of 932 MPa whereas, the same was found to attain 446 MPa under quasi-static loading conditons. Thus, establishing the rate depedency of Basalt/PEI composite. Damage studies revealed that Basalt/PEI undergoes brittle damage, typically resulting in shear plane angle of 42°−45°. SEM micrographs revealed brittle fibre failure and matrix free fibre bundles, indicating the importance of process parameter optimiation.