用神经网络同时优化复合材料结构与制造参数，提升强度且保证可打印性。
Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites
- 用三个隐式神经场统一表示形状、层序和纤维方向，实现联合优化。
- 生成的复合材料在实验中抗失效载荷最高提升33.1%。
- 适合需要高强度与可制造性平衡的3D打印复合材料设计者。
我们提出一种基于神经网络的计算框架，用于同时优化纤维增强热塑性复合材料的结构拓扑、弯曲层及路径方向，以实现强各向异性强度并确保可制造性。该框架采用三个隐式神经场分别表示几何形状、层序序列和纤维取向，将设计目标（如各向异性强度、结构体积）与可制造性约束（如机械运动控制、层曲率、层厚）统一建模为可微分的优化过程。通过将这些目标作为损失函数，确保最终复合材料在保持优异力学性能的同时，适配多种多轴丝材3D打印设备。物理实验表明，本方法生成的复合材料相比逐次优化结构与制造流程的方案，失效载荷最高提升33.1%。
原文摘要 · Abstract (English)
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.
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