4.7 Article

Optimization with manufacturing constraints for composite laminates reinforced by curvilinear fibers through a parametric level set method

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COMPOSITE STRUCTURES
卷 321, 期 -, 页码 -

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ELSEVIER SCI LTD
DOI: 10.1016/j.compstruct.2023.117310

关键词

Design optimization; Composite laminate; Variable stiffness; Curvilinear fiber; Manufacturing constraints; Level set method

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This paper presents a design optimization method for composite laminates reinforced by curvilinear fibers, considering manufacturing constraints. The method uses a parametric level set approach and compactly supported radial basis functions (CS-RBFs) to define the fiber paths in each layer of the laminate. Constraints, such as gradient norm and contour curvature radius, are considered to maintain structural integrity. Numerical examples are provided to demonstrate the effectiveness of the proposed method in compliance minimization and eigen-frequency maximization.
This paper presents the design optimization with manufacturing constraints for composite laminates reinforced by curvilinear fibers by using a parametric level set method. In each layer of the composite laminate, a level set function is defined by using the compactly supported radial basis functions (CS-RBFs), and the iso-contours of each level set function represent the fiber paths in one layer of the laminate. By this means, the fiber orientation at an arbitrary point in a layer can be computed by the orientation of the tangent vector of the iso-contour passing through this point. To avoid structure defects (such as gap/overlap, wrinkling and delamination) that would arise if the fiber paths are not parallel or their curvature is too large, the gradient norm and contour curvature radius of the level set functions are considered as constraints in the design optimization. The p -norm method is used to aggregate the constraints to improve the optimization efficiency. The coefficients of CS-RBFs are considered as design variables. Several numerical examples of compliance minimization and eigen-frequency maximization are investigated to verify the proposed method.

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