Strength-constrained simultaneous optimization of topology and fiber orientation of fiber-reinforced composite structures for additive manufacturing

Abstract

Additive manufacturing (AM) enables flexible fabrication of lightweight fiber-reinforced composite (FRC) structures with topological optimized geometries and fiber orientations. However, premature failure may occur if the manufactured FRC structure is not properly designed against stress distribution. The present study develops a strength-constrained optimization algorithm by considering the 3D printed filament embedded with fiber to be orthotropic material. The Tsai–Wu criterion is incorporated in the topological and fiber orientation optimization process. The element density and the fiber angle are formulated as design variables to simultaneously optimize the distribution of matrix and fiber. Meanwhile, both design variables are filtered to prevent checkerboard patterns of matrix elements and to ensure the continuity of fiber angle. A P-norm approach combined with an adaptive normalization scheme is employed to achieve the global strength constraint and reduce the difference between the P-norm value and the actual maximum Tsai–Wu value. Sensitivity analysis of objective function and strength-constraint conditions with respect to design variables is carried out and it serves as a basis for the method of moving asymptotes. Topological optimization of three typical numerical examples under different loading conditions are conducted and the effectiveness of the proposed method is validated. 73.7% decrease in maximum Tsai–Wu value of the optimized L-shaped beam as well as a slight increase (∼0.2%) in compliance are obtained in comparison with the compliance-based design result.

More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites

“…In this subsection, we provide results from our method and baselines on several additional shapes. The shape designs are inspired by sketches from SketchGraphs [Seff et al 2020], a large-scale dataset of sketches of real-world CAD models, as well as shapes from existing works [Ma et al 2022;Shafighfard et al 2019]. We use a Laplacian regularizer weight 𝑤 lap = 5 × 10 −7 , and the results are shown in Figure 16, with every dotted line a Dirichlet boundary condition.…”

“…See Hu [2021] for a survey (called "free material optimization"). The most common approach for orientation optimization is to set density and orientation as design variables and optimize an objective such as compliance [Chu et al 2021;da Silva et al 2020;Jung et al 2022] or the Tsai-Wu failure criterion [Ma et al 2022] with a gradient-based optimizer. To address the checkerboard pattern issue (periodicity of the orientation variables), researchers usually use filtering [Andreassen et al 2011] to smooth the design variable field (e.g., through a weighted average of neighboring elements).…”