RESUMO
In this research, we delve into the fascinating dynamics of projectiles and their interactions with materials, with a keen focus on residual velocity - the speed a projectile retains after striking a target. This parameter is pivotal, especially when considering the design of protective barriers in various environments. Traditional methods of gauging residual velocity have been cumbersome, resource-intensive, and occasionally inconsistent. To address these challenges, we introduce an innovative approach using an Artificial Neural Network (ANN) model through MATLAB R2021a. This computerized tool, trained on a rich dataset from prior research, can predict residual velocities by considering multiple factors, including the initial speed of the projectile, its material and shape, and the thickness of the target. This paper meticulously details the development, training, and validation of the ANN model, highlighting its superior accuracy when compared to traditional methods like the Recht-Ipson model. The developed ANN model demonstrated remarkable performance compared to the Recht-Ipson model. During training, it exhibited a Mean Absolute Percentage Error (MAPE) of 0.0259 and a Root Mean Squared Error (RMSE) of 1.5993. For validation, MAPE was 0.0295, and RMSE was 2.2056. In contrast, the Recht-Ipson model displayed higher errors, with MAPE and RMSE values of 0.2349 and 14.1791, respectively. Furthermore, we discuss the potential of the ANN model in predicting not just residual velocities but also absorbed energy, showcasing its versatility. The practical implications of our findings are vast. From designing safer infrastructures in urban settings to enhancing armour systems in military applications, the ANN model's predictions can be a cornerstone for innovation.
RESUMO
The effect of separation of fiber orientation through the thickness of thin composites on their low velocity impact response is studied. The composites are prepared using unidirectional glass fiber reinforced epoxy through hand layup followed by vacuum bagging. The two dissimilar layups of composites considered have separation of composite layers with fibers having the same and different orientations through the thickness. The composites are subjected to low velocity impact using a drop-weight testing machine. The composites are evaluated using different performance parameters such as damage degree, first damage and maximum forces, first cracking energy, bending stiffness, elastic strain energy, elastic, residual and maximum displacements, permanent deformation, square-root delaminated area, delamination length and width, and contact duration. Among the two composites, it is observed that [90/-45/45/0] composite in which two layers with fibers having 90° and 0° orientations separating by two layers with fibers having -45° and 45° orientations, levels of deformation are lower and recorded force and square-root delaminated area are higher and lower, respectively for the same level of impact energy. Whereas, in case of [0/90/90/0] composite in which two layers with fibers having 0° orientations separating by two layers with fibers having 90° orientations, recorded force is lower and deformation and square-root delaminated area are higher. The [0/90/90/0] composite is having a comparatively more lateral spread of delamination and inter-layer opening than that of [90/-45/45/0] composite considering extensional and bending stiffnesses along longitudinal (A11, D11) and transverse (A22, D22) directions. This facilitates that lateral spread of damage within composite can be decreased by separating two layers of composite with 90° and 0° fiber orientations by two layers with -45° and 45° fiber orientations, i.e., [90/-45/45/0] composite.