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

TL;DR

This paper introduces a neural network framework that co-optimizes the design and manufacturability of fiber-reinforced composites, improving their strength and manufacturability for 3D printing.

Contribution

It presents a novel neural co-optimization approach that simultaneously optimizes structural topology, layer geometry, and fiber orientation for enhanced composite performance.

Findings

Achieves up to 33.1% increase in failure load.

Integrates design and manufacturability objectives into a differentiable framework.

Demonstrates effectiveness across diverse 3D printing hardware.

Abstract

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.