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A set of non-linear optimisation algorithms are combined with a finite element simulation code to analyse the energy absorption and elastic stress wave propagation problem in multilayer/multimaterial armour systems under ballistic impacts. An Abaqus Python script is used to simulate the ballistic event and to generate the variables and post-processing outputs necessary for the integration with the optimisation algorithms. A number of modelling strategies are considered and three optimisation algorithms are used: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The performance and efficiency of each algorithm are assessed through four benchmark tests with different levels of complexity. A multi-objective optimisation procedure is proposed that uses the most efficient algorithm based on every single-objective formulation, variables and constraints from the benchmark tests, resulting in a highly non-linear problem. The proposed optimisation methods successfully achieve the study purposes both in the simulation of generic ballistic impacts and in the quality of the optimised solutions, demonstrating the potential for this type of optimisation method on terminal ballistic applications, serving as a standpoint for further studies into higher energy impacts and material non-linearities. [ABSTRACT FROM AUTHOR]

This research manuscript describes the process of degradation of an Armox 600 armour plate during the impact of a 5.56x45 mm SS109 projectile. The creation process of the numerical FEM model of the projectile is presented. The projectile impact angle is set between 15° and 90°, and this phenomenon is investigated via numerical and experimental approaches. The experiment is conducted under the same conditions as the numerical approach to validate the FEM model. The experiments are conducted using a high-speed camera. This research manuscript presents the influence of the projectile impact angle on the degradation of the armour plate and its protection capability for different angles. The results demonstrate the dependence of the transferred energy on the armour plate, speed of the particles after impact, and trace dimensions on the armour plate for different impact angles. [ABSTRACT FROM AUTHOR]

The analysis was performed on six commonly used 5.56 x 45 mm projectile types (M193, M855, M855A1, L31A1, M995, and AP45). The projectile's impact velocity (chosen as 900 m/s) was the same in the examination process. This made it possible to assess how the projectile's design has affected its performance against a hard steel target. These evaluations included numerical simulations (Ansys Autodyn) of projectile impacts. Using the available experimental data, the materials in the computational model were first validated. A short description and CAD model of projectiles is also given. Relevant conclusions were reported. [ABSTRACT FROM AUTHOR]

A coupled framework of ballistic simulations and an optimized machine learning (ML) model was developed to accurately predict the kinetic energy reduction of a projectile impacting a target. ML models can require a significant number of data points for proper training, testing, and validation. High-performance computing (HPC) resources can be used to simulate the ballistic impacts of various projectiles against several different target materials using appropriate physics-based hydrocodes. Computational modeling can explore areas where experiments would naturally be cost-prohibitive. These hydrocodes can evaluate large parametric spaces varying the projectile and target variables that are required to train an ML model. In this study a large, generated set of data points was used to develop an optimized artificial neural network (ANN) algorithm to create a fast-running model without prior knowledge of the mathematical relationships between all the input and output variables. The optimized ANN model was developed using Optuna in an HPC environment to tune the hyperparameters needed for the ANN model. This fast-running ML model could then be leveraged to investigate designing optimized targets that could protect against different types of projectiles. The results of this work showed that the optimized ANN model predicted the kinetic energy reduction with a mean absolute percentage error of 2.7% across the validation data. Overall, the optimized ANN model showed excellent agreement across the range of data considered by the computational models. [ABSTRACT FROM AUTHOR]

Copyright of Problems of Mechatronics. Armament, Aviation, Safety Engineering / Problemy Mechatroniki. Uzbrojenie, lotnictwo, Inżynieria Bezpieczeństwa is the property of Index Copernicus International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computed tomography (CT) may have a crucial role in the forensic documentation and analysis of firearm injuries. The aim of this forensic ballistics case study was to explore whether two types of expanding bullets and a full metal-jacketed bullet could be differentiated by inspecting bullet fragments and fragmentation pattern in CT. Three types of.30 caliber bullets (full metal-jacketed Norma Jaktmatch, expanding full-copper Norma Ecostrike, and expanding soft-point Norma Oryx) were test fired from a distance of 5 m to blocks of 10% ballistic gelatine. CT scans of the blocks were obtained with clinical equipment and metal artifact reduction. Radiopaque fragments were identified and fragmentation parameters were obtained from the scans (total number of fragments, maximum diameter of the largest fragment, distance between entrance and the closest fragment, length of the fragment cloud, and maximum diameters of the fragment cloud). The fragmentation patterns were additionally visualized by means of 3D reconstruction. In CT, the bullet types differed in several fragmentation parameters. While the expanding full-copper bullet Ecostrike left behind only a single fragment near the end of the bullet channel, the soft-point Oryx had hundreds of fragments deposited throughout the channel. For both expanding bullets Ecostrike and Oryx, the fragments were clearly smaller than those left behind by the full metal-jacketed Jaktmatch. This was surprising as the full metal-jacketed bullet was expected to remain intact. The fragment cloud of Jaktmatch had similar mediolateral and superoinferior diameters to that of Oryx; however, fragments were deposited in the second half of the gelatine block, and not throughout the block. This case study provides a basis and potential methodology for further experiments. The findings are expected to benefit forensic practitioners with limited background information on gunshot injury cases, for example, those that involve several potential firearms or atypical gunshot wounds. The findings may prove beneficial for both human and wildlife forensics. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting (XGBoost), artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GP), on two common terminal ballistics' problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. To achieve this we utilise two datasets, each consisting of approximately 1000 samples, collated from public release sources. We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To circumvent this, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not. The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types, target materials and thicknesses, and impact conditions significantly more diverse than that achievable from any existing analytical approach. Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster. We provide some general guidelines throughout for the development, application, and reporting of ML models in terminal ballistics problems. [ABSTRACT FROM AUTHOR]

The ongoing pursuit of space and hypersonic flight continues to expose critical gaps in the understanding of material behavior under hypervelocity impact (HVI) and hypersonic flow conditions. Such limitations pose serious risks for aerospace vehicles, spacecraft, hardened structures, defensive systems, etc. Consequently, the development of materials and systems that can endure HVIs and hypersonic flight is a major obstacle in the quest for sustainable space exploration, reusable air-breathing hypersonic vehicles, and enduring protective structures. HVIs (≥ 3.0 km/s) can induce severe material deformation, erosion, fracturing, fragmentation, melting, vaporization, and sublimation. At the same time, hypersonic (≥ Mach 5) vehicles may be subjected to intense thermal and mechanical loads. Addressing these grand challenges requires a multifaceted and interdisciplinary approach, combining well-designed experiments with physics-based analytical and numerical modeling. Studying material behavior under HVIs and hypersonic conditions has been facilitated by two-stage light gas gun (2SLGG) aeroballistic ranges for almost seven decades. This current study surveys over 90 2SLGG aeroballistic ranges operational since 1990 to assess global launch and experimental capabilities. The 2SLGG's origins and research applications are explored, highlighting its significance in various fields, including shock physics, planetary science/defense, military defense, nuclear physics, hypersonic vehicle survivability and performance, and spacecraft micro-meteoroid/orbital debris protection. A summary of relevant HVI phenomena is presented to underscore the importance of 2SLGGs and to elucidate similarities and differences among various 2SLGG aeroballistic ranges and their supporting methods/tools. The 2SLGG's working principles are explained, and configurations and operations are compared. Modifications resulting in "three-stage light gas guns" are briefly mentioned for completeness. The full range of current 2SLGG performance capabilities is assessed with impact kinetic energies ranging from ∼ 10 joules to nearly 100 megajoules, and the facility survey results are used to explain the variations in aeroballistic range tankage, experiment types, research applications, and diagnostic systems. Finally, an overview of 2SLGG performance prediction methods is provided, featuring notable empirical, analytical, and numerical approaches. • Survey of over 90 2SLGG aeroballistic ranges active since 1990 worldwide. • 2SLGG's impact in multiple fields like military and space highlighted. • Detailed 2SLGG working principles and three-stage gun modifications explained. • 2SLGG performance spectrum from 10 J to 100 MJ detailed. • Variability in 2SLGG ranges and their scientific uses described. [ABSTRACT FROM AUTHOR]