A Validated Python-Based Approach for Composite Plate Analysis with Geometric Discontinuities

Abstract

This study presents the development and validation of an open-source finite element framework implemented in Python for the analysis of laminated composite plates using First Order Shear Deformation Theory (FSDT). The formulation employs graded quadrilateral meshing with eight-noded serendipity elements and incorporates full laminate constitutive modeling through computation of the A, B, and D stiffness matrices along with transverse shear effects. The framework enables automated geometry generation for plates with geometric discontinuities, global stiffness assembly, displacement solution, ply-level stress recovery, and contour visualization. To assess numerical accuracy, the developed solver is benchmarked against the layered shell formulation (SHELL181) in ANSYS under identical geometry, mesh density, material properties, boundary conditions, and loading. Comparisons are performed for in-plane and transverse displacements as well as normal stress components in laminated plies for a composite plate containing a central circular cutout. The Python-based results demonstrate strong agreement with the commercial solver, with displacement deviations below 1% and stress deviations within 5%. The study highlights the feasibility of using open-source computational tools for small- to medium-scale composite structural analyses, reducing dependency on high-cost licensed software for preliminary design studies and parametric investigations. Furthermore, the developed framework is extended toward the generation of high-fidelity finite element datasets to enable data-driven modeling. Specifically, the solver infrastructure is being utilized to create structured simulation databases for training Graph Neural Network (GNN)-based predictive models aimed at replacing computationally expensive finite element simulations. This integration establishes a pathway toward rapid surrogate modeling of composite structures while preserving physics-informed accuracy.

Introduction

Laminated composite plates are widely used in aerospace, automotive, and energy applications due to their high stiffness-to-weight ratio and customizable mechanical properties. Structural behavior depends on stacking sequence, fiber orientation, and geometric discontinuities like cutouts, which introduce stress concentrations. Accurate displacement and stress prediction is critical for design and failure assessment.

This work develops a Python-based finite element framework for laminated plate analysis using First Order Shear Deformation Theory (FSDT). It employs eight-noded quadrilateral elements with five degrees of freedom per node, incorporating membrane, bending, coupling, and transverse shear stiffness through A, B, and D matrices. The solver handles geometry generation, mesh refinement near cutouts, stiffness assembly, boundary condition application, solution of displacements, and ply-level stress recovery.

Validation against a commercial finite element solver (ANSYS layered shell elements) shows excellent agreement, with displacement errors below 1% and stress deviations under 5%, confirming the framework’s reliability. The Python implementation also enables data-driven modeling for surrogate prediction using Graph Neural Networks, offering a flexible, low-cost alternative for preliminary design, parametric studies, and educational purposes.

Conclusion

In this study, a Python-based finite element framework for laminated composite plate analysis has been developed and validated using First Order Shear Deformation Theory. The implementation includes laminate constitutive modeling, mesh generation, global stiffness assembly, and ply-level stress recovery within an open computational environment. Validation against a commercial solver shows strong agreement, with displacement deviations below 1% and stress deviations within 5%, confirming the numerical accuracy of the approach. The framework offers a cost-effective solution for preliminary and medium-scale analyses and serves as a foundation for data-driven modeling. It enables the generation of high-fidelity simulation datasets for training Graph Neural Network (GNN)-based models aimed at approximating finite element responses with significantly reduced computational cost.

References

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Copyright

Copyright © 2026 Dr. Shailesh S. Kadre, Harsha Vardhan Sirisolla, Satyanand Velpukonda, Sivaram Ydesi, Rohit Gold Puspala. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.