TOBACO: Topology Optimization via Band-limited Coordinate Networks for Compositionally Graded Alloys
Aaditya Chandrasekhar, Stefan Knapik, Deepak Sharma, John Reidy, Ian McCue, Jian Cao, Wei Chen

TL;DR
This paper presents a novel topology optimization framework for designing Compositionally Graded Alloys using band-limited coordinate neural networks, enabling controlled material gradation and high-resolution, mesh-independent designs.
Contribution
It introduces a neural network-based method that implicitly enforces gradation constraints in CGA design, improving upon traditional explicit constraint approaches.
Findings
Effective control of compositional gradation in CGAs.
Mesh-independent, high-resolution design extraction.
Demonstrated success in elastic and thermo-elastic optimization examples.
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…
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