Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier
- URL: http://arxiv.org/abs/2409.13908v2
- Date: Sat, 28 Sep 2024 20:15:48 GMT
- Title: Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier
- Authors: Jaewan Park, Shashank Kushwaha, Junyan He, Seid Koric, Qibang Liu, Iwona Jasiuk, Diab Abueidda,
- Abstract summary: This paper presents a novel framework for inverse multi-material design based on nonlinear stress-strain responses.
By incorporating multiple materials, plasticity, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials.
- Score: 2.624762732763203
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are often composed of multiple materials that repeat geometric patterns. While traditional inverse design approaches have shown potential, they struggle to map nonlinear material behavior to multiple possible structural configurations. This paper presents a novel framework leveraging video diffusion models, a type of generative artificial Intelligence (AI), for inverse multi-material design based on nonlinear stress-strain responses. Our approach consists of two key components: (1) a fields generator using a video diffusion model to create solution fields based on target nonlinear stress-strain responses, and (2) a structure identifier employing two UNet models to determine the corresponding multi-material 2D design. By incorporating multiple materials, plasticity, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials commonly seen in real-world applications. It offers a promising solution for generating next-generation metamaterials with finely tuned mechanical characteristics.
Related papers
- Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions.
Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements.
We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
arXiv Detail & Related papers (2024-11-06T23:53:34Z) - Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials [0.0]
This paper defines several design spaces for chiral metamaterials, including the ligament contact angles, the ligament shape, and circle radius.
We then leverage machine learning approaches, and specifically Bayesian optimization, to determine optimally performing designs satisfying maximal non-reciprocity or stiffness asymmetry.
Our analysis of the underlying mechanisms reveals that chiral metamaterials that can display multiple different contact states under loading in different directions are able to simultaneously exhibit both high non-reciprocity and stiffness asymmetry.
arXiv Detail & Related papers (2024-04-19T23:39:56Z) - Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials [41.97258566607252]
This paper proposes a fast inverse design method, whose core is an advanced deep generative AI algorithm, to generate voxel-based mechanical metamaterials.
Specifically, we use the self-conditioned diffusion model, capable of generating a microstructure with a resolution of $1283$ to approach the specified homogenized matrix in just 3 seconds.
arXiv Detail & Related papers (2024-01-24T16:31:50Z) - Scalable Diffusion for Materials Generation [99.71001883652211]
We develop a unified crystal representation that can represent any crystal structure (UniMat)
UniMat can generate high fidelity crystal structures from larger and more complex chemical systems.
We propose additional metrics for evaluating generative models of materials.
arXiv Detail & Related papers (2023-10-18T15:49:39Z) - FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations.
We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
arXiv Detail & Related papers (2023-04-28T21:48:31Z) - Differentiable graph-structured models for inverse design of lattice
materials [0.0]
Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science.
We propose a new computational approach using graph-based representation for regular and irregular lattice materials.
arXiv Detail & Related papers (2023-04-11T18:00:21Z) - DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure
reconstruction from extremely small data sets [110.60233593474796]
DA-VEGAN is a model with two central innovations.
A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture.
A custom differentiable data augmentation scheme is developed specifically for this architecture.
arXiv Detail & Related papers (2023-02-17T08:49:09Z) - How to See Hidden Patterns in Metamaterials with Interpretable Machine
Learning [82.67551367327634]
We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials.
Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
arXiv Detail & Related papers (2021-11-10T21:19:02Z) - Intelligent multiscale simulation based on process-guided composite
database [0.0]
We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning.
We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
arXiv Detail & Related papers (2020-03-20T20:39:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.