Ply-drop design of non-conventional composites using Bayesian optimization

TL;DR

This paper presents a Bayesian optimization approach to design non-conventional ply-drop angles in composite structures, improving mechanical performance and manufacturing efficiency over traditional layering methods.

Contribution

It introduces a novel optimization method combining classical laminate theory and Bayesian techniques for non-standard ply-drop angle design.

Findings

Optimized layup angles improve stiffness and failure criteria.

Method reduces manufacturing time.

Effective for complex surface structures.

Abstract

Automated Fiber Placement (AFP) technology provides a great ability to efficiently produce large carbon fiber reinforced composite structures with complex surfaces. AFP has a wide range of tow placement angles, and the users can design layup angles so that they can tailor the performance of the structure. However, despite the design freedom, the industry generally adopts a layering of 0 deg, 90 deg, and plus-minus 45 deg ply-drop angles. Here, we demonstrate the optimization of ply-drop angles of non-conventional composites. Specifically, we use classical laminate theory and Bayesian optimization to achieve better layup angles in terms of stiffness, Tsai-Wu failure criteria, and manufacturing time. Our approach shows its effectiveness in designing carbon fiber composite structures using unconventional angles in terms of both mechanical properties and production efficiency. Our method has the potential to be used for more complex scenarios, such as the production of curved surfaces and the utilization of finite element analysis.