Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

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

Predicting the effects of microstructure on creep strength in Ceramic Matrix Composite using Data driven Modelling

Jet Aircraft Engines turbine blades, Rocket engines, Missiles, re-entry vehicles strive to improve performance. Ceramic Matrix composites (CMC’s) replace Nickel Super-alloys because of the numerous novel advantages as weight reduction, operating at higher temperatures, producing higher thrust by reducing the cooling air diverted from thrust. The traditional material science engineering takes a long time to generate these advanced ceramic matrix composite with the tailored required performance. With the rapid development of machine learning, it is possible to use neural network to build models to predict the performance of CMC’s. A reduced order, data driven, predictive model to quantify the creep strength in continuous CMC using the machine learning tools is proposed and explored. A framework is developed to quantify the importance of microstructural parameters on strength. The stochastic microstructure attributes considered includes the fiber diameter, fiber spacing, interface material, interface thickness and volume fraction. The elastic responses of the instantiated microstructures are characterized using finite element analysis (FEA). Results from the FEA will be used as the ground truth to calibrate and validate a data-driven machine learning (ML) model. The quantified stochastic microstructure attributes will be correlated with the statistics of the simulated response. The predictive capabilities of the model for a new microstructure will be demonstrated

A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials

Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We employ deep learning to model the mesoscale energy localization of shock-initiated EM microstructures upon which prediction results are used to supply reaction progress rate information to the macroscale SDT simulation. The proposed multiscale modeling framework is divided into two stages. First, a physics-aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock-initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub-grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high-performance and safer energetic materials

Deep Learning-Guided Prediction of Material’s Microstructures and Applications to Advanced Manufacturing

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss. In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) to regress microstructures against numerical processing parameters. After training, the algorithm grasps the mapping between microstructures and processing parameters and can infer the microstructure according to an unseen processing parameter value. This CGAN-enabled approach consumes low computational power for prediction and does not require manual feature extraction. A regression-based conditional Wasserstein generative adversarial network (RCWGAN) is developed, and its microstructure prediction capability is demonstrated on a synthetic micrograph dataset. Several important hyperparameters, including loss function, model depth, number of training epochs, and size of the training set, are systematically studied and optimized. After optimization, prediction accuracy in various microstructural features is over 92%. Then the RCWGAN is validated on a scanning electron microscopy (SEM) micrograph dataset obtained from laser-sintered alumina. Data augmentation is applied to ensure an adequate number of training samples. Different regularization technologies are studied. It is found that gradient penalty can preserve the most details in the generated microstructure. After training, the RCWGAN is able to predict the microstructure as a function of laser power. In-situ microstructure monitoring using the RCWGAN is proposed and demonstrated. Obtaining microstructure information during fabrication could enable accurate microstructure control. It opens the possibility of fabricating a new kind of materials with novel functionalities. The RCWGAN is integrated into a laser sintering system equipped with a camera to demonstrate this novel application. Surface-emission brightness is captured by the camera during the laser sintering process and fed to the RCWGAN for online microstructure prediction. After training, the RCWGAN learns the mapping between surface-emission brightness and microstructures and can make prediction in seconds. The prediction accuracy is over 95% in terms of average grain size