Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending

12/01/2021
by   Yu-Chin Chan, et al.
0

To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversity-based freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.

READ FULL TEXT

page 8

page 13

page 17

page 18

page 19

page 20

page 24

research
06/11/2021

Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization

For natural frequency optimization of engineering structures, cellular c...
research
12/05/2021

Enhancing Data-driven Multiscale Topology Optimization with Generalized De-homogenization

De-homogenization is becoming an effective method to significantly exped...
research
06/27/2020

Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process

The data-driven approach is emerging as a promising method for the topol...
research
07/28/2021

Mechanical Cloak via Data-Driven Aperiodic Metamaterial Design

Mechanical cloaks are materials engineered to manipulate the elastic res...
research
01/31/2023

Universal Topological Regularities of Syntactic Structures: Decoupling Efficiency from Optimization

Human syntactic structures are usually represented as graphs. Much resea...
research
05/31/2021

Isogeometric shape optimization for scaffold structures

The development of materials with specific structural properties is of h...
research
02/21/2022

T-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning

Inspired by the recent success of deep learning in diverse domains, data...

Please sign up or login with your details

Forgot password? Click here to reset