Using machine learning to predict the ballistic response of structures to projectile impact

Ballistic loading is a primary risk in both civil and military defence applications, where successfully predicting the dynamic response of a material to impact is a fundamental component of the design of safe and fit-for-purpose protective structures. Approaches to understand the response to ballistic impact conventionally revolve around experimental tests, whereby the material or structure of interest is subject to impact by a projectile across a range of impact velocities. However, experimental testing is expensive and incurs large costs due to the destructive nature of the testing and the specialist equipment required. Numerical tools, such as the Finite Element (FE) method, play an important role by filling the gaps left sparse by experimental results and contribute towards the complete dynamic material characterisation campaign. This thesis considers an alternative to FE models by using Machine Learning (ML) techniques that learn directly from the available ballistic data. Specifically, the thesis considers the use of Multi-Layer Perceptron (MLP) models to predict the ballistic response of multi-layered targets to impact but its primary intention is to explore the value that generative networks can bring to the ballistic domain. This thesis shows how Generative Adversarial Networks (GANs) can be used to supplement sparse ballistic datasets by generating new samples representative of the dataset that it was trained on, but also how they can be used to predict key ballistic parameters for engineering design such as the ballistic limit velocity, vbl. And finally, how conditional-GANs (cGANS) can be utilised to allow the network to be conditioned on additional auxiliary information such as class labels that refer to a specific property relevant to the ballistic data thus allowing the cGAN to generate new samples specific to the class label given. This allows the trained cGAN to generate data for classes that are not present in the training set and conduct its own material characteristic campaign. The justification for using ML practices for in the ballistic domain lies in the idea that numerical models are adjusted such that the output is consistent with the results from experimental testing. There is therefore an opportunity for research to explore whether ML techniques can capture that same distribution by training on the ballistic data directly

Development of Predictive Ballistic Models for Hypervelocity Impact on Sandwich Panel Satellite Structures

Sandwich panels are widely used in the design of uninhabited satellites and, in addition to having a structural function can often serve as shielding, protecting the satellites’ equipment from hypervelocity impacts (HVI) of orbital debris, and micrometeoroids. This thesis aims to provide: a comprehensive review of HVI experimental studies for honeycomb- and open-cell foam-cores; an examination of available predictive models used to assess the panels’ ballistic limits; as well as signify the influence of honeycomb-core parameters, such as cell size and foil thickness, as well as core material, on the ballistic performance of honeycomb-core sandwich panels (HCSP) when subject to HVI scenarios. To study the influence of HCSP parameters, two predictive models: a dedicated ballistic limit equation (BLE)—based on the Whipple shield BLE—and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP were developed. A database composed of physical and numerical simulations allowed for BLE fitting and ANN training. The ANN was developed using MATLAB’s Deep Learning Toolbox framework and was tuned using a comprehensive parametric study to optimize the ANN architecture, including such parameters as the activation function, the number of hidden layers and the number of nodes per layer. The predictive models were verified using a new set of simulation data and achieved low error percentage in comparison when predicting the ballistic limits of HCSP, ranging from 1.13% to 5.58% (BLE) and 0.67% and 7.27% (ANN), respectfully

Use of artificial neural networks to optimize stacking sequence in UHMWPE protections

This article belongs to the Special Issue Polymeric Materials for Energy Absorption Applications.The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%.This research was funded by Comunidad de Madrid of Spain, grant number IND2017/IND7762 and The APC was funded by this project

Graphene Nano-Composites For Hypervelocity Impact Applications

The Low Earth Orbit (LEO) is a harsh environment cluttered with natural meteoroids and man-made debris, which can travel at velocities approaching 15 km/s. Most space activities within the LEO will encounter this environment. Thus, the spacecraft and its hardware must be designed to survive debris impact. This research introduces new procedures to produce a nano-composite material with mortar-brick nano-structure inspired from nacre. Nacre-like composites were successfully manufactured, based on three host polymers, with a wide range of graphene concentrations. The manufactured exfoliated graphene nano-platelet, embedded in a host polymer, provided good potential for enhancement of the hypervelocity impact (HVI) shield resistance. The nano-composites are suggested for use as a coating. Moreover, explicit dynamic finite element studies were conducted for further investigation of the hypervelocity impact of the graphene-based coatings in order to understand the effect of the coating on the crater formation and the exit velocity. This dissertation presents the results of the characterization and numerical sensitivity study of the developed material parameters. The numerical simulations were performed by implementing Autodyn smooth particle hydrodynamics. This study provides innovative, low-weight shielding enhancements for spacecraft, as well as other promising applications for the manufactured nano-composites

Dinamičko mehanička svojstva hibridnih nanokompozitnih materijala

Predmet istraživanja ove doktorske disertacije pripada oblasti nanomateijala i nanotehnogija koja je u trendu savremenih istraživanja. Posebno su intenzivna istraživanja u oblasti polimernih nanokompozita gde tradicionalno slabe strane polimera (niske vrednosti parametara mehaničke čvrstoće i loša termostabilnost) se značajno poboljšavaju primenom malog udela nano punioca i ojačanja uz neznatan porast gustine. Razvijena je metoda dizajniranja strukture nanokompozitnih balističkih materijala sa gledišta poboljšanja njihovih svojstava otpornosti pri udarima visoke energije. Proučeni su uslovi dobijanja laminarnih kompozitnih materijala p-aramid/poli (vinil butiral). Poli (vinil butiralni) sloj nanošen je u obliku disperzije poli (vinil butirala) i nano čestica SiOR2R u etil-alkoholu, pri čemu su korišćene modifikovane i nemodifikovane čestice SiOR2 Rsa vezujućim agensom-AMEO silanom. Na taj nači je utvrđen veliki značaj modifikacije nano čestica SiOR2R sa silanima na mehanička, termička i antibalistička svojstva dobijenih hibridnih nanokompozitnih materijala. Savremena istraživanja u ovoj oblasti usmerena su u pronalaženju mehanizama zaustavljanja rasta prsline modifikovanjem strukture na nano nivou što je i predmet ove doktorske disertacije. Proučavanja u okviru ove disertacije bila su usmerena na istraživanja mehanizama apsorpcije energije u nanokompozitima pri udarnim opterećenjima visoke energije i ponašanje nano čestica kao konstituenata u strukturi hibridnih kompozitnih materijala. Sinteza ovih nanokompozitnih materijala izvršiće se primenom koloidnih suspenzija koje se karakterišu ekstremnim porastom viskoznosti pri velikim brzinama smicanja kojima su izloženi pri udarnim naprezanjima. Originalnost ideje se ogleda što je princip hibridizacije primenjen na izradu laminatnih balističkih ploča sa laminama koje su različito nanomodifikovane a samim tim i sa različitim svojstvima. Značaj ove ideje je što različito nanomodifikovane lamine omogućavaju izradu funkcionalno gradijentnih kompozitnih materijla od nano do mikro nivoa. Ciljevi ove disertacije su višestruki: 1) proučavanje mehanizama procesiranja nano prahova različitih oksida u različitim disperzionim sredstvima prema klasičnim metodama i savremenim metodama modifikovanja površine čestica; 2) eksperimentalna istraživanje uticaja procesnih uslova brizganja i toplog presovanja hibridnih nonokompozita sa tkaninama od aramidnih vlakana sa različitim udelom modifikovanih nanočestica na njihova dinamickomehanička svojstva (modul sačuvane i izgubljene energije i tangens gubitaka) u različitom temperaturnom intervalu pri različitim frekvencijama); 3) eksperimentalna istraživanje uticaja procesnih uslova brizganja i toplog presovanja hibridnih laminatnih nonokompozita sa matricom od poli (vinil butirala) sa razlicitim udelom modifikovanih cestica silicijum dioksida na makromehanicka svojstva (Jungov modul elasticnosti, zatezna cvrstoca, prekidno izduženje); 4) eksperimentalna ispitivanja otpornosti na razaranje dobijenih hibridnih nanokompozitnih materijala na udar velikim energijama i brzinama (standardna balisticka ispitivanja sa municijom u realnim uslovima).The purpose of this dissertation is to investigate the effects of lamination and hybrid soft armor systems through ballistic impact. The investigation was carried out by using dynamic mechanical analysis and actual ballistic testing. The most important conclusions derived from this research are that lamination of the systems with very low resin content are superior to multiple non-laminated systems, and this advance could be improved further by hybrid systems using nanomodified fabric layers on the impact side and relatively tighter woven fabrics between the layers. This dissertation reports the preparation of SiOR2R and TiOR2R/poly (vinyl butyral) nanocomposites with enhanced dynamic mechanical properties. Silica and titania nanoparticles were introduced in the matrix as the neat powder and as colloidal sol using the melt mixing process. Composites reinforced with colloidal sol silica and titania showed higher mechanical properties than the ones reinforced with as-received particles. When sol TiOR2R particles are used, the highest increase of storage modulus of about 54% is obtained for 5 wt% loading, while for sol SiOR2R, the storage modulus increases with the addition of nanosilica with the largest increase of about 99% for 7 wt% loading. In addition, nanocomposites were introduced within Kevlar/PVB composites. The addition of 5 wt% silica and titania colloidal sol lead to the remarkable increase of the storage modulus for about 98 and 65%, respectively. Largest contribution of nanoreinforcements in lowering the glass transition temperature is observed for 7 wt% loading of TiOR2R and SiOR2R colloidal sol. This study reports the manufacture of new fabric forms from the preparation of hybrid laminated multi-axial composites with enhanced thermo-mechanical properties. Thermal and dynamic mechanical analysis of polymer matrix films and fabricated hybrid composites were employed in order to determine the optimal material composition and reinforcement content for composites with improved viscoelastic properties. The introduction of 5 wt. % silica nanoparticles in a composite of p-aramid– poly(vinyl butyral) led to significant improvements in the mechanical properties, and the addition of silane coupling agents yielded maximal values of the storage modulus for hybrid nanocomposites. The introduction of silane led to a better dispersion and deagglomeration of SiOR2R particles and to the formation of chemical bonds between organic and inorganic constituents, or p-aramid–poly(vinyl butyral) composites. In this way, the mobility of macromolecules was reduced, which can be seen from the decreasing value of damping factor for the p-aramid–poly(vinyl butyral) composite. Analysis of the glass transition temperature of the composite with amino-functionalized silica nanoparticles revealed improved thermal stability in addition to the aforementioned mechanical properties of the tested materials

Ballistic Evaluation of Carbon Nanotube Sheet Material in Multifunctional Applications

Significant development of carbon nanotubes has occurred since they were first studied in the 1990\u27s. Attempts to capture the phenomenal molecular properties in practical applications are gaining ground as new methods of producing CNTs have been developed. This thesis sought to determine if the addition of commercially produced CNT sheets to thin carbon fiber panels improved the ballistic properties of the panel. The difference between 0 and 4 CNT sheets was studied. The hypothesis was that inte- grating CNT sheets into the laminate would increase the projectile energy absorbed by the panel and reduce the damage to the panel incurred by the impact. Damage to the panel was assessed through delamination area and EMI shielding degradation. Projectile energy absorption was measured through residual velocity measurement and ballistic limit modeling. A gas gun shooting half inch steel ball bearings simulated high-speed debris impact on the panel. This study found that the addition of one or two CNT sheets provided a marginal increase of up to 0.7 joules of projectile energy reduction by the panel. In general it was not found that the CNT sheets significantly contributed to the ability of the panel to stop a projectile at the quantities studied. It was found that with four CNT sheets in the panel, the EMI shielding after impact at 350 ft/s was improved by as much as 40 dB compared to the panel with no CNT sheets

Aeronautic structures reinforced with graphene subjected to dynamic loadings. Analysis and modeling

Mención Internacional en el título de doctorEn la industria aeronáutica, los materiales compuestos son esenciales para muchos componentes estructurales debido a su excelente relación resistencia/peso, situándose como principales en muchos casos. Sin embargo, la desventaja que presentan en algunas solicitaciones como los impactos hacen que su diseño y optimización sigan siendo un campo muy relevante de la ingeniería. En la búsqueda de nuevas soluciones, doparlos con nanomateriales como el grafeno puede generar una notable diferencia en su resistencia gracias a la mejora de propiedades mecánicas. Esta tesis doctoral emplea como base el método de fabricación y los materiales estandarizados para el borde de ataque del Airbus A350, y estudia como le afecta la inclusión de grafeno, considerando distintos porcentajes y tiempos de producción del nanomaterial. Para ello, se han desarrollado múltiples ensayos mecánicos y no mecánicos que comprueban qué mejoras aporta el grafeno y qué aplicaciones reales puede suponer. El estudio abarca desde el análisis de la resina curada sin fibras hasta los ensayos del laminado frente a cargas dinámicas de impacto, pasando por un análisis de la intercara entre la fibra y la matriz. Además, el material dopado se obtiene sin modificación alguna del proceso de fabricación estandar, lo que permitiría una industrialización directa del nuevo material. En primer lugar, se presentan los métodos de fabricación, tanto del grafeno como de los componentes que se someterán a esayo. Una exfoliación in-situ de nanofibras de carbono permite obtener el grafeno directamente en la resina que posteriormente se inyecta, sin alterar las fases del proceso de moldeo por transferencia de resina (RTM). Posteriormente, se explican todas y cada una de las técnicas experimentales empeladas: Los ensayos a la resina neta, los ensayos para evaluar la intercara (ILSS), el método de compresión tras impacto (CAI), y el impacto de proyectiles dúctiles a alta velocidad. Finalmente, los resultados de la tesis muestran que el grafeno tiene un efecto positivo en las propiedades mecánicas evaluadas, y que aumentar el porcentaje de grafeno o el tiempo de producción también inluye en las mejoras. Por último, se desarrollaron modelos predictivos que ayudan a comprender el origen de las diferencias y permiten aportar más conclusiones.In the aeronautic industry, composite materials are essential for many structural components due to their excellent lightweight/strength ratio. However, the drawbacks they present in some scenarios such as impacts make their design and optimization a very relevant field for engineers. In the investigation for new solutions, doping them with nanomaterials such as graphene can provide an important difference thanks to the improvement of mechanical properties. This PhD thesis employs the standard manufacturing method and materials for the leading edge of the Airbus A350, and evaluates how the inclusion of graphene affects the reference material, considering different percentages and production times of the nanomaterial. To do this, multiple mechanical and non-mechanical tests have been developed to analyze what improvements graphene brings and what real applications it can imply. The study goes from the analysis of the cured resin without fibers to the tests of the laminate against impact dynamic loads, with an analysis of the interface between the fiber and the matrix. In addition, the doped material is obtained without any modification of the standard manufacturing process, which would allow for direct industrialization of the new material. Firstly, manufacturing methods of both, graphene and coupons that will be tested are presented. An in-situ exfoliation of carbon nanofibers allows obtaining the graphene directly in the resin that is subsequently injected without altering the phases of the resin transfer molding process (RTM). Then, all the experimental techniques used are detailed: tests on the neat resin, how to evaluate the interface (ILSS), the compression after impact method (CAI) and the impact of soft projectiles at high velocity. Finally, thesis results show that graphene has a positive effect on the mechanical properties, and that increasing the percentage of added graphene or the production time also affects. Finally, predictive models were developed to help in the understanding of the improvements and to provide further conclusions.Programa de Doctorado en Ingeniería Mecánica y de Organización Industrial por la Universidad Carlos III de MadridPresidente: José Antonio Loya Lorenzo.- Secretario: César Merino Sánchez.- Vocal: Filipe Teixeira-Dia

Multi-scale modelling and material characterisation of textile composites for aerospace applications

Textile composites offer an excellent alternative to metallic alloys in the aerospace engineering due to their high specific stiffness and strength, superb fatigue strength, excellent corrosion resistance and dimensional stability. In order to successfully apply these materials to engineering problems, a methodology to characterise and predict the constitutive response of these materials is essential. The lack of the modelling tools for modern textile composites that would facilitate systematic analysis and characterisation of these materials hinders the wide adoption of such material systems in engineering applications. This defines the focus of the project as represented in this thesis. A multi-scale modelling methodology has been established for the material characterisation and representing the constitutive response of the material at a macroscale. For material characterisation at micro- and mesoscales, an automated material characterisation toolbox, UnitCells©, has been employed and substantially developed in both the scope and complexity through this project.When applying this toolbox, the user selects the required type of a textile or unidirectional reinforcements and provides a parametric input, based on which a finite element model of a unit cell for the composites is generated automatically. The effective properties that can be predicted using this toolbox include stiffness, thermal expansion coefficient, thermal and electric conductivities, static strength and dynamic strength (associated with deformation localisation as the limit of the applicability of unit cells but a conservative estimate of the material strength). There are seven types of microscale models and eleven types of mesoscale models available in the toolbox at present. To represent a constitutive relationship for textile composites at a macroscale, the artificial neural network (ANN) algorithm has been adapted and developed into a useful modelling tool, referred to as the ANN system. A criterion defining an ultimate failure of the material has been proposed. The outcome has made it possible for a user defined material subroutine to be established which can be employed in the analysis of structures made of such textile composites by providing the effective constitutive behaviour of them in a most efficient manner. As a validation, ANN system was used to predict the critical velocities of three types of layer-to-layer interlock 3D woven composite panel subject to ballistic loading. The predicted results matched well with the testing results. Furthermore, as an illustration of potential capability, the ANN system has been used to simulate impact of a textile composite fan blade containment casing in an idealised fan blade off scenario. Through the project, the capability of predicting the impact behaviour of textile composites has been established. This involves unit cell modelling at micro-/mesoscales for material characterisation, strength prediction with due consideration of strain rate sensitivity of the constituent materials, and ANN system to deliver the characterised constitutive relationship in terms of a user defined material subroutine for practical applications at macroscale, such as structural impact analysis