Dynamic impact and tensile strength characteristics of novel shear thickening fluid (STF)-treated fabric and modeling tensile strength using artificial intelligence

A series of shear thickening fluids (STFs) were prepared based on dispersed fumed silica in virgin polyethylene glycol (V/PEG) and modified PEGs by malonic acid (M/PEG) and tartaric acid (T/PEG). Rheological examination revealed that modification of PEG to a higher chain length of medium molecules remarkably improves STF peak viscosity and decreases the critical shear rate. High-velocity impact resistance of V/STF, M/STF, and T/STFtreated fabric was investigated at different layers and impact velocities and found significant improvement over neat fabric targets. Investigation of the results of two-layer samples showed the energy dissipation of V/STF, M/STF, and T/STF-treated fabrics at an impact velocity of 240 m/s is improved by 30.04%, 40.60%, and 46.06%, compared to the neat fabric, respectively. Furthermore, the tensile strength test revealed that these STFs fill the spaces between the fibers and make the fabric stronger and more resistant to breaking. In addition, a Linear/ Nonlinear artificial intelligence regression analysis was performed. The results showed a strong correlation between composite mechanical response and speed. The proposed model can be further used to predict material behavior at other tensile speeds.