Postmortem Analysis Using Different Sensors and Technologies on Aramid Composites Samples after Ballistic Impact.

This work focuses on the combination of two complementary non-destructive techniques to analyse the final deformation and internal damage induced in aramid composite plates subjected to ballistic impact. The first analysis device, a 3D scanner, allows digitalising the surface of the tested specimen. Comparing with the initial geometry, the permanent residual deformation (PBFD) can be obtained according to the impact characteristics. This is a significant parameter in armours and shielding design. The second inspection technique is based on computed tomography (CT). It allows analysing the internal state of the impacted sample, being able to detect possible delamination and fibre failure through the specimen thickness. The proposed methodology has been validated with two projectile geometries at different impact velocities, being the reaction force history on the specimen determined with piezoelectric sensors. Different loading states and induced damages were observed according to the projectile type and impact velocity. In order to validate the use of the 3D scanner, a correlation between impact velocity and damage induced in terms of permanent back face deformation has been realised for both projectiles studied. In addition, a comparison of the results obtained through this measurement method and those obtained in similar works, has been performed in the same range of impact energy. The results showed that CT is needed to analyse the internal damage of the aramid sample; however, this is a highly expensive and time-consuming method. The use of 3D scanner and piezoelectric sensors is perfectly complementary with CT and could be relevant to develop numerical models or design armours.

An Experimental and Numerical Study on the Influence of Helices of Screw Piles Positions on Their Bearing Capacity in Sandy Soils.

Helical piles became a popular foundation technique, and as a result of environmental restrictions, they have become increasingly widely used. However, due to the high cost of experimentation, the influence of the number of helices and their positions on the pile-bearing capacity has not been sufficiently studied. The present study performed compression and lateral load tests on helical piles of the same diameter but with one, two, and three round helices in known sandy soil. The results from the experiments are compared with those from numerical simulations that use the mesh-free RBF method and the Winkler-Fuss approach to model how the pile and ground interact. The results are generalized to suggest an engineering equation that can predict the best pile configuration in sandy soil.

Additive Manufacturing of Head Surrogates for Evaluation of Protection in Sports.

Head impacts are a major concern in contact sports and sports with high-speed mobility due to the prevalence of head trauma events and their dire consequences. Surrogates of human heads are required in laboratory testing to safely explore the efficacy of impact-mitigating mechanisms. This work proposes using polymer additive manufacturing technologies to obtain a substitute for the human skull to be filled with a silicone-based brain surrogate. This assembly was instrumentalized with an Inertial Measurement Unit. Its performance was compared to a standard Hybrid III head form in validation tests using commercial headgear. The tests involved impact velocities in a range centered around 5 m/s. The results show a reasonable homology between the head substitutes, with a disparity in the impact response within 20% between the proposed surrogate and the standard head form. The head surrogate herein developed can be easily adapted to other morphologies and will significantly decrease the cost of the laboratory testing of head protection equipment, all while ensuring the safety of the testing process.

Numerical Modelling of Ballistic Impact Response at Low Velocity in Aramid Fabrics.

In this study, the effect of the impact angle of a projectile during low-velocity impact on Kevlar fabrics has been investigated using a simplified numerical model. The implementation of mesoscale models is complex and usually involves long computation time, in contrast to the practical industry needs to obtain accurate results rapidly. In addition, when the simulation includes more than one layer of composite ply, the computational time increases even in the case of hybrid models. With the goal of providing useful and rapid prediction tools to the industry, a simplified model has been developed in this work. The model offers an advantage in the reduced computational time compared to a full 3D model (around a 90% faster). The proposed model has been validated against equivalent experimental and numerical results reported in the literature with acceptable deviations and accuracies for design requirements. The proposed numerical model allows the study of the influence of the geometry on the impact response of the composite. Finally, after a parametric study related to the number of layers and angle of impact, using a response surface methodology, a mechanistic model and a surface diagram have been presented in order to help with the calculation of the ballistic limit.

Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach.

Local delamination is the most undesirable damage associated with drilling carbon fiber reinforced composite materials (CFRPs). This defect reduces the structural integrity of the material, which affects the residual strength of the assembled components. A positive correlation between delamination extension and thrust force during the drilling process is reported in literature. The abrasive effect of the carbon fibers modifies the geometry of the fresh tool, which increases the thrust force and, in consequence, the induced damage in the workpiece. Using a control system based on an artificial neural network (ANN), an analysis of the influence of the tool wear in the thrust force during the drilling of CFRP laminate to reduce the damage is developed. The spindle speed, feed rate, and drill point angle are also included as input parameters of the study. The training and testing of the ANN model are carried out with experimental drilling tests using uncoated carbide helicoidal tools. The data were trained using error-back propagation-training algorithm (EBPTA). The use of the neural network rapidly provides results of the thrust force evolution in function of the tool wear and cutting parameters. The obtained results can be used by the industry as a guide to control the impact of the wear of the tool in the quality of the finished workpiece.