Beyond Double-Double theory: n-Directional stacking sequence optimisation in composite laminates

Journal Article (2025)
Author(s)

Jr José Humberto S. Almeida (LUT University)

Emilia Balonek (LUT University)

Saullo Giovani Pereira Castro (TU Delft - Group Giovani Pereira Castro)

Research Group
Group Giovani Pereira Castro
DOI related publication
https://doi.org/10.1016/j.compstruct.2025.119586
More Info
expand_more
Publication Year
2025
Language
English
Research Group
Group Giovani Pereira Castro
Volume number
373
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This paper presents a novel stacking sequence design framework for composite laminates, extending the recently established Double-Double (DD) laminate theory developed by Stephen Tsai. By introducing and evaluating n-Double (n-D) layouts, ranging from single-angle (D) sequences to multi-directional designs such as DD, DDD, and DDDD; this study expands the design space for laminated composite structures, enabling improved trade-offs between buckling resistance and failure strength. A genetic algorithm (GA) is used to optimise the stacking sequences of 48- and 64-layer graphite/epoxy laminates under biaxial and uniaxial compressive loading across a range of geometric aspect ratios. Results show that while GA-based free-angle designs yield the highest buckling loads, structured DDDD configurations achieve similar or superior failure performance and maintain a high level of robustness across geometric variations. The DDDD designs also approximate GA-level buckling performance, with significantly improved regularity and manufacturability. These findings highlight the benefit of generalising Tsai’s DD theory towards n-D layouts, providing a systematic, practical, and high-performing approach to laminate optimisation.

Files

License info not available