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

TL;DR

This paper introduces a data-driven topology optimization framework that blends multiple microstructure classes to create functionally graded, multiscale structures with enhanced design flexibility and efficiency.

Contribution

It proposes a novel multiclass shape blending scheme that generates smoothly graded microstructures without needing class compatibility or connectivity constraints.

Findings

Demonstrates versatility with compliance and shape matching examples

Shows effectiveness of class diversity on design quality

Transforms microscale problems into low-dimensional, efficient optimizations

Abstract

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.