Multi-objective Generative Design of Three-Dimensional Composite
Materials
- URL: http://arxiv.org/abs/2302.13365v1
- Date: Sun, 26 Feb 2023 17:45:44 GMT
- Title: Multi-objective Generative Design of Three-Dimensional Composite
Materials
- Authors: Zhengyang Zhang, Han Fang, Zhao Xu, Jiajie Lv, Yao Shen, Yanming Wang
- Abstract summary: We report a Wasserstein generative adversarial network (MDWGAN) to implement inverse designs of 3D composite structures.
Our framework is capable of tuning the properties of the generated composites in multiple aspects, while keeping the selected features of different kinds of structures.
- Score: 12.258353990252312
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Composite materials with 3D architectures are desirable in a variety of
applications for the capability of tailoring their properties to meet multiple
functional requirements. By the arrangement of materials' internal components,
structure design is of great significance in tuning the properties of the
composites. However, most of the composite structures are proposed by empirical
designs following existing patterns. Hindered by the complexity of 3D
structures, it is hard to extract customized structures with multiple desired
properties from large design space. Here we report a multi-objective driven
Wasserstein generative adversarial network (MDWGAN) to implement inverse
designs of 3D composite structures according to given geometrical, structural
and mechanical requirements. Our framework consists a GAN based network which
generates 3D composite structures possessing with similar geometrical and
structural features to the target dataset. Besides, multiple objectives are
introduced to our framework for the control of mechanical property and isotropy
of the composites. Real time calculation of the properties in training
iterations is achieved by an accurate surrogate model. We constructed a small
and concise dataset to illustrate our framework. With multiple objectives
combined by their weight, and the 3D-GAN act as a soft constraint, our
framework is proved to be capable of tuning the properties of the generated
composites in multiple aspects, while keeping the selected features of
different kinds of structures. The feasibility on small dataset and potential
scalability on objectives of other properties make our work a novel, effective
approach to provide fast, experience free composite structure designs for
various functional materials.
Related papers
- Smooth Like Butter: Evaluating Multi-Lattice Transitions in Property-Augmented Latent Spaces [0.0]
This work implements and evaluates a hybrid geometry/property Variational Autoencoder (VAE) for generating multi-lattice transition regions.
In our study, we found that hybrid VAEs demonstrate enhanced performance in maintaining stiffness continuity through transition regions.
arXiv Detail & Related papers (2024-07-10T22:28:13Z) - OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction [54.706361479680055]
We introduce the OpenMaterial dataset, comprising 1001 objects made of 295 distinct materials.
OpenMaterial provides comprehensive annotations, including 3D shape, material type, camera pose, depth, and object mask.
It stands as the first large-scale dataset enabling quantitative evaluations of existing algorithms on objects with diverse and challenging materials.
arXiv Detail & Related papers (2024-06-13T07:46:17Z) - StructRe: Rewriting for Structured Shape Modeling [63.792684115318906]
We present StructRe, a structure rewriting system, as a novel approach to structured shape modeling.
Given a 3D object represented by points and components, StructRe can rewrite it upward into more concise structures, or downward into more detailed structures.
arXiv Detail & Related papers (2023-11-29T10:35:00Z) - Physics-Constrained Neural Network for Design and Feature-Based
Optimization of Weave Architectures [0.6144680854063939]
We present a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties of weave architectures.
We show that the proposed PCNN can effectively predict weave architecture for the desired modulus with higher accuracy than several baseline models considered.
arXiv Detail & Related papers (2022-09-19T16:16:45Z) - Remixing Functionally Graded Structures: Data-Driven Topology
Optimization with Multiclass Shape Blending [15.558093285161775]
We propose a data-driven framework for multiclass functionally graded structures.
The key is a new multiclass shape blending scheme that generates smoothly graded microstructures.
It transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes.
arXiv Detail & Related papers (2021-12-01T16:54:56Z) - 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) - Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model
for Protein Design [70.27706384570723]
We propose Fold2Seq, a novel framework for designing protein sequences conditioned on a specific target fold.
We show improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design.
The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges.
arXiv Detail & Related papers (2021-06-24T14:34:24Z) - Deep Generative Modeling for Mechanistic-based Learning and Design of
Metamaterial Systems [20.659457956055366]
We propose a novel data-driven metamaterial design framework based on deep generative modeling.
We show in this study that the latent space of VAE provides a distance metric to measure shape similarity.
We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems.
arXiv Detail & Related papers (2020-06-27T03:56:55Z) - Revealing the Invisible with Model and Data Shrinking for
Composite-database Micro-expression Recognition [49.463864096615254]
We analyze the influence of learning complexity, including the input complexity and model complexity.
We propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data.
We develop three parameter-free modules to integrate with RCN without increasing any learnable parameters.
arXiv Detail & Related papers (2020-06-17T06:19:24Z) - Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from
a Single RGB Image [102.44347847154867]
We propose a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives.
Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives.
Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.
arXiv Detail & Related papers (2020-04-02T17:58:05Z) - 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.