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Additive manufacturing (AM) has emerged as a promising approach to achieve energetic materials (EMs) with intricate geometries and controlled microstructures, which are crucial for safety and performance optimization. However, current AM methods still face limitations such as limited densities and inadequate solids loading. To overcome these limitations, we have developed a pressure‐assisted binder jet (PBJ) process that has the potential to allow for the fabrication of intricate EMs while preserving their desired properties. This study aims to investigate the effects of printing parameters on the microstructures and properties of EMs, including density, solids loading, mechanical properties, and heterogeneity. Our results demonstrate that the PBJ process achieves exceptional properties in EMs, including densities up to 83.4 % and solids loading up to 95.4 %, surpassing those achieved by existing AM processes. Furthermore, the mechanical properties of the fabricated EMs are comparable to those achieved using conventional fabrication techniques, including a compressive strength of 3.32 MPa, a Young's modulus of 16.68 MPa, a Poisson's ratio of 0.45, a shear modulus of 5.73 MPa, and a bulk modulus of 21.01 GPa. Various test cases were printed to showcase the ability of the PBJ process to create EMs with complex structures and exceptional properties. Micro‐computed tomography was employed to analyze the influence of printing parameters on the internal composition and microstructures of the printed specimens. [ABSTRACT FROM AUTHOR]

The response of a wide class of heterogeneous energetic materials (HEs) to loads is determined by dynamics at the meso-scale, i.e., by physicochemical processes in their underlying microstructure. Structure–property–performance (S–P–P) linkages for such materials can be developed in a multi-scale framework, connecting the physics and thermophysical properties at the meso-scale to response at the macro-scale. Due to the inherent stochasticity of the microstructure, ensembles of microstructures are required to conduct meso-scale simulations to establish S–P–P linkages. Here, a deep neural network-based method called deep feature representation is applied to generate a range of material microstructures from heterogeneous energetic materials to metal foams and metallic mixtures. The method allows for the generation of stochastic microstructures using a single real microstructure as the input and is not limited to low packing density or topological complexity of solids. In its application to pressed energetic materials, we show that qualitative and quantitative features of real (i.e., imaged) microstructures are captured in the synthetic microstructures. Therefore, a stochastic ensemble of synthetic microstructures can be created for use in reactive meso-scale simulations to relate the microstructures of HEs to their performance. While the focus is on pressed HE microstructures, we also show that the method is general and useful for generating microstructures for in silico experiments for a wide range of composite/multiphase materials, which can be used to establish S–P–P linkages. [ABSTRACT FROM AUTHOR]

Meso-scale simulations of pressed energetic materials are performed using synthetic microstructures generated using deep feature representation, a deep convolutional neural network-based approach. Synthetic microstructures are shown to mimic real microstructures in the statistical representation of global and local features of micro-morphology for three different classes of pressed HMX with distinctive micro-structural characteristics. Direct numerical simulations of shock-loaded synthetic microstructures are performed to calculate the meso-scale reaction rates. For all three classes, the synthetic microstructures capture the effect of morphological uncertainties of real microstructures on the response to shock loading. The calculated reaction rates for different classes also compare well with those of the corresponding real microstructures. Thus, the article demonstrates that machine-generated ensembles of synthetic microstructures can be employed to derive structure–property–performance linkages of a wide class of real pressed energetic materials. The ability to manipulate the synthetic microstructures using deep learning-based approaches then provides an opportunity for material designers to develop and manufacture pressed energetic materials that can yield targeted performance. [ABSTRACT FROM AUTHOR]

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone. However, synthesizing realistic microstructures with realistic microstructural attributes is highly challenging. Thus, it is often oversimplified via rough approximations that may yield an inaccurate representation of the physical world. Here, we propose a novel deep learning method that can synthesize realistic three-dimensional microstructures with controlled structural properties using the combination of generative adversarial networks (GAN) and actor-critic (AC) reinforcement learning. The GAN-AC combination enables the generation of microstructures that not only resemble the appearances of real specimens but also yield user-defined physical quantities of interest (QoI). Our validation experiments confirm that the properties of synthetic microstructures generated by the GAN-AC framework are within a 5% error margin with respect to the target values. The scientific contribution of this paper resides in the novel design of the GAN-AC microstructure generator and the mathematical and algorithmic foundations therein. The proposed method will have a broad and substantive impact on the materials community by providing lenses for analyzing structure-property-performance linkages and for implementing the notion of 'materials-by-design'. [ABSTRACT FROM AUTHOR]

Shock load analysis of two different samples of pressed HMX energetic material is performed using the Eulerian compressible multimaterial code SCIMITAR3D. The numerical framework uses an image to computation approach to perform shock analysis on real microstructures of the energetic samples. Image processing algorithms are applied on SEM images of both samples to implicitly represent the microstructures using level set functions. The chemical decomposition of HMX is modeled using the Henson-Smilowitz multi-step kinetic mechanism. It is observed that microstructural characteristics play a crucial role in determining the ignition behavior of the energetic materials. For the applied shock loads and for the particular samples investigated, class III sample leads to initiation of chemical reaction and the class V sample does not ignite. It is also shown that the orientation of elongated voids with respect to incident shock load is an important factor contributing to the initiation of chemical reactions in the class III sample. This is explained by performing numerical experiments of elongated void oriented at different angles with respect to the shock load. Results show the importance of microstructural details, such as void size, distribution, and orientation for initiation. [ABSTRACT FROM AUTHOR]

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework. [ABSTRACT FROM AUTHOR]

Morphology and dynamics at the mesoscale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upscale unresolved sub-grid (mesoscale) physics and therefore encapsulate structure–property (S–P) linkages to predict performance at the macroscale. This work establishes a route to S–P linkage, proceeding all the way from imaged microstructures to flow computations in one unified level-set-based framework. Level sets are used to: (1) define embedded geometries via image segmentation; (2) simulate the interaction of sharp immersed boundaries with the flow field; and (3) calculate morphological metrics to quantify structure. Mesoscale dynamics is computed to calculate sub-grid properties, i.e., closure models for momentum and energy equations. The S–P linkage is demonstrated for two types of multi-material flows: interaction of shocks with a cloud of particles and reactive meso-mechanics of pressed energetic materials. We also present an approach to connect local morphological characteristics in a microstructure containing topologically complex features with the shock response of imaged samples of such materials. This paves the way for using geometric machine learning techniques to associate imaged morphologies with their properties. [ABSTRACT FROM AUTHOR]