FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network

TL;DR

This paper introduces a neural network-based topology optimization framework for continuous fiber-reinforced composites, enabling high-resolution fiber design and efficient sensitivity analysis, suitable for additive manufacturing.

Contribution

The paper presents a mesh-independent neural network approach for topology optimization of FRCs, improving fiber resolution and automating sensitivity analysis.

Findings

Neural network representation captures high-resolution fiber distributions.

The method improves computational efficiency over traditional approaches.

Optimized designs are suitable for high-resolution additive manufacturing.

Abstract

In this paper, we present a topology optimization (TO) framework to simultaneously optimize the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced composites (FRC). Current approaches in density-based TO for FRC use the underlying finite element mesh both for analysis and design representation. This poses several limitations while enforcing sub-element fiber spacing and generating high-resolution continuous fibers. In contrast, we propose a mesh-independent representation based on a neural network (NN) both to capture the matrix topology and fiber distribution. The implicit NN-based representation enables geometric and material queries at a higher resolution than a mesh discretization. This leads to the accurate extraction of functionally-graded continuous fibers. Further, by integrating the finite element simulations into the NN computational framework, we can leverage automatic differentiation for end-to-end automated sensitivity analysis, i.e., we no longer need to manually derive cumbersome sensitivity expressions. We demonstrate the effectiveness and computational efficiency of the proposed method through several numerical examples involving various objective functions. We also show that the optimized continuous fiber reinforced composites can be directly fabricated at high resolution using additive manufacturing.