# Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites

arXiv: https://arxiv.org/abs/2505.03779 · HTML: https://mlsanity.com/paper/2505.03779/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 81 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [On the use of Artificial Neural Networks in Topology Optimisation](https://mlsanity.com/paper/2208.02563/) (2022)
- [Two-Scale Topology Optimization with Microstructures](https://mlsanity.com/paper/1706.03189/) (2017)
- [Neural Metamaterial Networks for Nonlinear Material Design](https://mlsanity.com/paper/2309.10600/) (2023)
- [Neural Slicer for Multi-Axis 3D Printing](https://mlsanity.com/paper/2404.15061/) (2024)
- [Learning Based Toolpath Planner on Diverse Graphs for 3D Printing](https://mlsanity.com/paper/2408.09198/) (2024)
- [Singularity-aware motion planning for multi-axis additive manufacturing](https://mlsanity.com/paper/2103.00273/) (2021)
- [Topology optimization considering the distortion in additive manufacturing](https://mlsanity.com/paper/2008.06153/) (2020)
- [Synthesis of Frame Field-Aligned Multi-Laminar Structures](https://mlsanity.com/paper/2104.05550/) (2021)
- [Topology optimization with a closed cavity exclusion constraint for additive manufacturing based on the fictitious physical model approach](https://mlsanity.com/paper/2112.08839/) (2021)
- [Support Generation for Robot-Assisted 3D Printing with Curved Layers](https://mlsanity.com/paper/2302.05510/) (2023)
- [Differentiable Stripe Patterns for Inverse Design of Structured Surfaces](https://mlsanity.com/paper/2305.13841/) (2023)
- [Efficient Inverse-designed Structural Infill for Complex Engineering Structures](https://mlsanity.com/paper/2307.09518/) (2023)
- [Toolpath Generation for High Density Spatial Fiber Printing Guided by Principal Stresses](https://mlsanity.com/paper/2410.16851/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors](https://mlsanity.com/paper/2602.17783/) (2026)

## Abstract (arXiv)

We propose a neural network-based computational framework for the simultaneous optimization of structural topology, curved layers, and path orientations to achieve strong anisotropic strength in fiber-reinforced thermoplastic composites while ensuring manufacturability. Our framework employs three implicit neural fields to represent geometric shape, layer sequence, and fiber orientation. This enables the direct formulation of both design and manufacturability objectives - such as anisotropic strength, structural volume, machine motion control, layer curvature, and layer thickness - into an integrated and differentiable optimization process. By incorporating these objectives as loss functions, the framework ensures that the resultant composites exhibit optimized mechanical strength while remaining its manufacturability for filament-based multi-axis 3D printing across diverse hardware platforms. Physical experiments demonstrate that the composites generated by our co-optimization method can achieve an improvement of up to 33.1% in failure loads compared to composites with sequentially optimized structures and manufacturing sequences.