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

2025-04-30

AI Summary

For fiber-reinforced thermoplastic composites, simultaneous optimization of anisotropic strength-driven topology, manufacturable curved-layer sequencing, and fiber orientation remains challenging in multi-axis fused deposition manufacturing (FDM). Method: We propose a structure–process co-optimization framework leveraging three coupled implicit neural fields to jointly represent geometry, layer sequence, and fiber orientation. A differentiable geometric modeling pipeline is developed, integrating multi-objective loss functions with multi-axis printing constraints for end-to-end optimization. Contribution/Results: Experimental validation demonstrates a 33.1% increase in failure load over conventional sequential optimization approaches. The method ensures printability on multi-axis FDM systems, exhibits strong generalizability across diverse hardware platforms, and maintains high engineering applicability without requiring post-processing or manual intervention.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Simultaneous optimization of topology, layers, and fiber orientations
• Ensuring manufacturability in fiber-reinforced composites
• Improving mechanical strength via integrated neural network framework

Innovation

• Neural network optimizes topology, layers, and fiber orientations
• Implicit neural fields integrate design and manufacturability objectives
• Differentiable optimization enhances strength and 3D printing compatibility