TOBACO: Topology Optimization via Band-limited Coordinate Networks for Compositionally Graded Alloys

2025-08-13

AI Summary

This study addresses the key challenge of designing lightweight, high-strength topologies for compositionally graded alloys (CGAs) under additive manufacturing constraints—specifically, how to achieve optimal structural performance while respecting a prescribed maximum spatial composition gradient. We propose a band-limited coordinate network (BCN), an implicit neural representation of material composition fields. By constraining the frequency-domain bandwidth, the BCN intrinsically enforces gradient bounds without explicit penalty terms or post-processing, ensuring mesh independence, end-to-end differentiability, and high-fidelity geometric extraction. The framework unifies elastic and thermoelastic multiphysics topology optimization. Numerical experiments demonstrate that optimized CGA structures exhibit significantly enhanced load-bearing capacity under diverse mechanical and thermomechanical loading conditions, while rigorously satisfying manufacturability requirements imposed by additive manufacturing.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Compositionally Graded Alloys (CGAs) offer unprecedented design flexibility by enabling spatial variations in composition; tailoring material properties to local loading conditions. This flexibility leads to components that are stronger, lighter, and more cost-effective than traditional monolithic counterparts. The fabrication of CGAs have become increasingly feasible owing to recent advancements in additive manufacturing (AM), particularly in multi-material printing and improved precision in material deposition. However, AM of CGAs requires imposition of manufacturing constraints; in particular limits on the maximum spatial gradation of composition. This paper introduces a topology optimization (TO) based framework for designing optimized CGA components with controlled compositional gradation. In particular, we represent the constrained composition distribution using a band-limited coordinate neural network. By regulating the network's bandwidth, we ensure implicit compliance with gradation limits, eliminating the need for explicit constraints. The proposed approach also benefits from the inherent advantages of TO using coordinate networks, including mesh independence, high-resolution design extraction, and end-to-end differentiability. The effectiveness of our framework is demonstrated through various elastic and thermo-elastic TO examples.

Problem

• Designing optimized Compositionally Graded Alloys (CGAs) with controlled gradation
• Imposing manufacturing constraints on spatial composition gradation in AM
• Ensuring compliance with gradation limits using band-limited neural networks

Innovation

• Band-limited coordinate neural networks for gradation control
• Implicit compliance with manufacturing constraints via bandwidth regulation
• Mesh-independent high-resolution topology optimization framework