14 research outputs found
Towards deep generation of guided wave representations for composite materials
Laminated composite materials are widely used in most fields of engineering.
Wave propagation analysis plays an essential role in understanding the
short-duration transient response of composite structures. The forward
physics-based models are utilized to map from elastic properties space to wave
propagation behavior in a laminated composite material. Due to the
high-frequency, multi-modal, and dispersive nature of the guided waves, the
physics-based simulations are computationally demanding. It makes property
prediction, generation, and material design problems more challenging. In this
work, a forward physics-based simulator such as the stiffness matrix method is
utilized to collect group velocities of guided waves for a set of composite
materials. A variational autoencoder (VAE)-based deep generative model is
proposed for the generation of new and realistic polar group velocity
representations. It is observed that the deep generator is able to reconstruct
unseen representations with very low mean square reconstruction error. Global
Monte Carlo and directional equally-spaced samplers are used to sample the
continuous, complete and organized low-dimensional latent space of VAE. The
sampled point is fed into the trained decoder to generate new polar
representations. The network has shown exceptional generation capabilities. It
is also seen that the latent space forms a conceptual space where different
directions and regions show inherent patterns related to the generated
representations and their corresponding material properties
Smoothing the Rough Edges: Evaluating Automatically Generated Multi-Lattice Transitions
Additive manufacturing is advantageous for producing lightweight components
while addressing complex design requirements. This capability has been
bolstered by the introduction of unit lattice cells and the gradation of those
cells. In cases where loading varies throughout a part, it may be beneficial to
use multiple, distinct lattice cell types, resulting in multi-lattice
structures. In such structures, abrupt transitions between unit cell topologies
may cause stress concentrations, making the boundary between unit cell types a
primary failure point. Thus, these regions require careful design in order to
ensure the overall functionality of the part. Although computational design
approaches have been proposed, smooth transition regions are still difficult to
achieve, especially between lattices of drastically different topologies. This
work demonstrates and assesses a method for using variational autoencoders to
automate the creation of transitional lattice cells, examining the factors that
contribute to smooth transitions. Through computational experimentation, it was
found that the smoothness of transition regions was strongly predicted by how
closely the endpoints were in the latent space, whereas the number of
transition intervals was not a sole predictor.Comment: 23 Pages, 8 Figure
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Hybrid Geometry/Property Autoencoders for Multi-Lattice Transitions
Additive manufacturing has revolutionized structural optimization by enhancing component
strength and reducing material requirements. One approach used to achieve these improvements
is the application of multi-lattice structures. The performance of these structures heavily relies on
the detailed design of mesostructural elements. Many current approaches use data-driven design
to generate multi-lattice transition regions, making use of models that jointly address the geometry
and properties of the mesostructures. However, it remains unclear whether the integration of
mechanical properties into the data set for generating multi-lattice interpolations is beneficial
beyond geometry alone. To address this issue, this work implements and evaluates a hybrid
geometry/property machine learning model for generating multi-lattice transition regions. We
compare the results of this hybrid model to results obtained using a geometry-only model. Our
research determined that incorporating physical properties decreased the number of variables to
address in the latent space, and therefore improves the ability of generative models for developing
transition regions of multi-lattice structures.Mechanical Engineerin
Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models
Microstructure reconstruction serves as a crucial foundation for establishing
Process-Structure-Property (PSP) relationship in material design. Confronting
the limitations of variational autoencoder and generative adversarial network
within generative modeling, this study adopted the denoising diffusion
probability model (DDPM) to learn the probability distribution of
high-dimensional raw data and successfully reconstructed the microstructures of
various composite materials, such as inclusion materials, spinodal
decomposition materials, chessboard materials, fractal noise materials, and so
on. The quality of generated microstructure was evaluated using quantitative
measures like spatial correlation functions and Fourier descriptor. On this
basis, this study also successfully achieved the regulation of microstructure
randomness and the generation of gradient materials through continuous
interpolation in latent space using denoising diffusion implicit model (DDIM).
Furthermore, the two-dimensional microstructure reconstruction is extended to
three-dimensional framework and integrates permeability as a feature encoding
embedding. This enables the conditional generation of three-dimensional
microstructures for random porous materials within a defined permeability
range. The permeabilities of these generated microstructures were further
validated through the application of the Boltzmann method
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Variable-density cellular structures can overcome connectivity and
manufacturability issues of topologically optimized structures, particularly
those represented as discrete density maps. However, the optimization of such
cellular structures is challenging due to the multiscale design problem. Past
work addressing this problem generally either only optimizes the volume
fraction of single-type unit cells but ignoring the effects of unit cell
geometry on properties, or considers the geometry-property relation but builds
this relation via heuristics. In contrast, we propose a simple yet more
principled way to accurately model the property to geometry mapping using a
conditional deep generative model, named Inverse Homogenization Generative
Adversarial Network (IH-GAN). It learns the conditional distribution of unit
cell geometries given properties and can realize the one-to-many mapping from
geometry to properties. We further reduce the complexity of IH-GAN by using the
implicit function parameterization to represent unit cell geometries. Results
show that our method can 1) generate various unit cells that satisfy given
material properties with high accuracy (relative error <5%) and 2) improve the
optimized structural performance over the conventional topology-optimized
variable-density structure. Specifically, in the minimum compliance example,
our IH-GAN generated structure achieves an 84.4% reduction in concentrated
stress and an extra 7% reduction in displacement. In the target deformation
examples, our IH-GAN generated structure reduces the target matching error by
24.2% and 44.4% for two test cases, respectively. We also demonstrated that the
connectivity issue for multi-type unit cells can be solved by transition layer
blending
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
As the complexity of modern manufacturing technologies increases, traditional
trial-and-error design, which requires iterative and expensive simulations,
becomes unreliable and time-consuming. This difficulty is especially
significant for the design of hot-stamped safety-critical components, such as
ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure
manufacturability, scalar-based Artificial-Intelligence-empowered surrogate
modelling (SAISM) has been investigated and implemented, which can allow
real-time manufacturability-constrained structural design optimisation.
However, SAISM suffers from low accuracy and generalisability, and usually
requires a high volume of training samples. To solve this problem, an
image-based Artificial-intelligence-empowered surrogate modelling (IAISM)
approach is developed in this research, in combination with an
auto-decoder-based blank shape generator. The IAISM, which is based on a
Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field
of the as-formed component given an arbitrary blank shape. Excellent prediction
performance of IAISM is achieved with only 256 training samples, which
indicates the small-data learning nature of engineering AI tasks using
structured data representations. The trained auto-decoder, trained
Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank
optimisation by modifying the latent vector. The optimiser can rapidly find
blank shapes that satisfy manufacturability criteria. As a high-accuracy and
generalisable surrogate modelling and optimisation tool, the proposed pipeline
is promising to be integrated into a full-chain digital twin to conduct
real-time, multi-objective design optimisation.Comment: 32 pages, 11 figure
Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials
Combinatorial problems arising in puzzles, origami, and (meta)material design
have rare sets of solutions, which define complex and sharply delineated
boundaries in configuration space. These boundaries are difficult to capture
with conventional statistical and numerical methods. Here we show that
convolutional neural networks can learn to recognize these boundaries for
combinatorial mechanical metamaterials, down to finest detail, despite using
heavily undersampled training sets, and can successfully generalize. This
suggests that the network infers the underlying combinatorial rules from the
sparse training set, opening up new possibilities for complex design of
(meta)materials
Learning from Invalid Data: On Constraint Satisfaction in Generative Models
Generative models have demonstrated impressive results in vision, language,
and speech. However, even with massive datasets, they struggle with precision,
generating physically invalid or factually incorrect data. This is particularly
problematic when the generated data must satisfy constraints, for example, to
meet product specifications in engineering design or to adhere to the laws of
physics in a natural scene. To improve precision while preserving diversity and
fidelity, we propose a novel training mechanism that leverages datasets of
constraint-violating data points, which we consider invalid. Our approach
minimizes the divergence between the generative distribution and the valid
prior while maximizing the divergence with the invalid distribution. We
demonstrate how generative models like GANs and DDPMs that we augment to train
with invalid data vastly outperform their standard counterparts which solely
train on valid data points. For example, our training procedure generates up to
98 % fewer invalid samples on 2D densities, improves connectivity and stability
four-fold on a stacking block problem, and improves constraint satisfaction by
15 % on a structural topology optimization benchmark in engineering design. We
also analyze how the quality of the invalid data affects the learning procedure
and the generalization properties of models. Finally, we demonstrate
significant improvements in sample efficiency, showing that a tenfold increase
in valid samples leads to a negligible difference in constraint satisfaction,
while less than 10 % invalid samples lead to a tenfold improvement. Our
proposed mechanism offers a promising solution for improving precision in
generative models while preserving diversity and fidelity, particularly in
domains where constraint satisfaction is critical and data is limited, such as
engineering design, robotics, and medicine