361 research outputs found
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
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
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
The research and development cycle of advanced manufacturing processes
traditionally requires a large investment of time and resources. Experiments
can be expensive and are hence conducted on relatively small scales. This poses
problems for typically data-hungry machine learning tools which could otherwise
expedite the development cycle. We build upon prior work by applying
conditional generative adversarial networks (GANs) to scanning electron
microscope (SEM) imagery from an emerging manufacturing process, shear assisted
processing and extrusion (ShAPE). We generate realistic images conditioned on
temper and either experimental parameters or material properties. In doing so,
we are able to integrate machine learning into the development cycle, by
allowing a user to immediately visualize the microstructure that would arise
from particular process parameters or properties. This work forms a technical
backbone for a fundamentally new approach for understanding manufacturing
processes in the absence of first-principle models. By characterizing
microstructure from a topological perspective we are able to evaluate our
models' ability to capture the breadth and diversity of experimental scanning
electron microscope (SEM) samples. Our method is successful in capturing the
visual and general microstructural features arising from the considered
process, with analysis highlighting directions to further improve the
topological realism of our synthetic imagery
Microstructure Design of Multifunctional Particulate Composite Materials using Conditional Diffusion Models
This paper presents a novel modeling framework to generate an optimal
microstructure having ultimate multifunctionality using a diffusion-based
generative model. In computational material science, generating microstructure
is a crucial step in understanding the relationship between the microstructure
and properties. However, using finite element (FE)-based direct numerical
simulation (DNS) of microstructure for multiscale analysis is extremely
resource-intensive, particularly in iterative calculations. To address this
time-consuming issue, this study employs a diffusion-based generative model as
a replacement for computational analysis in design optimization. The model
learns the geometry of microstructure and corresponding stress contours,
allowing for the prediction of microstructural behavior based solely on
geometry, without the need for additional analysis. The focus on this work is
on mechanoluminescence (ML) particulate composites made with europium ions and
dysprosium ions. Multi-objective optimization is conducted based on the
generative diffusion model to improve light sensitivity and fracture toughness.
The results show multiple candidates of microstructure that meet the design
requirements. Furthermore, the designed microstructure is not present in the
training data but generates new morphology following the characteristics of
particulate composites. The proposed approach provides a new way to
characterize a performance-based microstructure of composite materials
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Artificial intelligence (AI) is rapidly emerging as an enabling tool for
solving various complex materials design problems. This paper aims to review
recent advances in AI-driven materials-by-design and their applications to
energetic materials (EM). Trained with data from numerical simulations and/or
physical experiments, AI models can assimilate trends and patterns within the
design parameter space, identify optimal material designs (micro-morphologies,
combinations of materials in composites, etc.), and point to designs with
superior/targeted property and performance metrics. We review approaches
focusing on such capabilities with respect to the three main stages of
materials-by-design, namely representation learning of microstructure
morphology (i.e., shape descriptors), structure-property-performance (S-P-P)
linkage estimation, and optimization/design exploration. We provide a
perspective view of these methods in terms of their potential, practicality,
and efficacy towards the realization of materials-by-design. Specifically,
methods in the literature are evaluated in terms of their capacity to learn
from a small/limited number of data, computational complexity,
generalizability/scalability to other material species and operating
conditions, interpretability of the model predictions, and the burden of
supervision/data annotation. Finally, we suggest a few promising future
research directions for EM materials-by-design, such as meta-learning, active
learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge
the gap between machine learning research and EM research
Data-Augmented Structure-Property Mapping for Accelerating Computational Design of Advanced Material Systems
abstract: Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
In this paper, we introduce a denoising diffusion algorithm to discover
microstructures with nonlinear fine-tuned properties. Denoising diffusion
probabilistic models are generative models that use diffusion-based dynamics to
gradually denoise images and generate realistic synthetic samples. By learning
the reverse of a Markov diffusion process, we design an artificial intelligence
to efficiently manipulate the topology of microstructures to generate a massive
number of prototypes that exhibit constitutive responses sufficiently close to
designated nonlinear constitutive responses. To identify the subset of
microstructures with sufficiently precise fine-tuned properties, a
convolutional neural network surrogate is trained to replace high-fidelity
finite element simulations to filter out prototypes outside the admissible
range. The results of this study indicate that the denoising diffusion process
is capable of creating microstructures of fine-tuned nonlinear material
properties within the latent space of the training data. More importantly, the
resulting algorithm can be easily extended to incorporate additional
topological and geometric modifications by introducing high-dimensional
structures embedded in the latent space. The algorithm is tested on the
open-source mechanical MNIST data set. Consequently, this algorithm is not only
capable of performing inverse design of nonlinear effective media but also
learns the nonlinear structure-property map to quantitatively understand the
multiscale interplay among the geometry and topology and their effective
macroscopic properties.Comment: 21 pages, 11 figure
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling
Acquiring reliable microstructure datasets is a pivotal step toward the
systematic design of materials with the aid of integrated computational
materials engineering (ICME) approaches. However, obtaining three-dimensional
(3D) microstructure datasets is often challenging due to high experimental
costs or technical limitations, while acquiring two-dimensional (2D)
micrographs is comparatively easier. To deal with this issue, this study
proposes a novel framework for 2D-to-3D reconstruction of microstructures
called Micro3Diff using diffusion-based generative models (DGMs). Specifically,
this approach solely requires pre-trained DGMs for the generation of 2D
samples, and dimensionality expansion (2D-to-3D) takes place only during the
generation process (i.e., reverse diffusion process). The proposed framework
incorporates a new concept referred to as multi-plane denoising diffusion,
which transforms noisy samples (i.e., latent variables) from different planes
into the data structure while maintaining spatial connectivity in 3D space.
Furthermore, a harmonized sampling process is developed to address possible
deviations from the reverse Markov chain of DGMs during the dimensionality
expansion. Combined, we demonstrate the feasibility of Micro3Diff in
reconstructing 3D samples with connected slices that maintain morphologically
equivalence to the original 2D images. To validate the performance of
Micro3Diff, various types of microstructures (synthetic and experimentally
observed) are reconstructed, and the quality of the generated samples is
assessed both qualitatively and quantitatively. The successful reconstruction
outcomes inspire the potential utilization of Micro3Diff in upcoming ICME
applications while achieving a breakthrough in comprehending and manipulating
the latent space of DGMs
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