Automated Fiber Placement Laminate Level Optimization: A Physics-Inspired Method to Analyze Through-Thickness Defect Interactions

With the growing adoption of composite materials in industries such as aerospace, automotive, naval, wind energy, and even sectors like sports and consumer goods, there has been a strong push towards improving manufacturing reliability and productivity. One key method that has gained significant traction is Automated Fiber Placement (AFP), an additive manufacturing technique valued for its precision, efficiency, and adaptability in producing complex composite parts. As industries continue to shift from traditional metal-based structures to more advanced composite materials, the need for efficient and high- quality manufacturing solutions has become even more critical, prompting a focus on automation and optimization within the AFP lifecycle. This led to the development of the Computer Aided Process Planning (CAPP) software. The CAPP software aims to support process planners by identifying optimal starting points and layup strategies for each ply. Additionally, Ply-Level Optimization (PLO) seeks to assess ply quality by examining predicted defects such as gaps, overlaps, steering, and angle deviation. Laminate-Level Optimization (LLO) provides a much more demanding task, aiming to minimize defect stacking and promote defect dispersion throughout a laminate, enhancing manufacturing efficiency and part quality. This thesis proposes a novel approach for AFP process planning, providing the framework for integration within the CAPP software, enabling users to design and optimize laminate layups based on design criteria, Margin of Safety (MoS), and defect propagation. CAPP integrates defect predictions generated by CGTech’s VERICUT Composite Programming, which are then discretized to simulate through-thickness defect stacking. Additionally, MoS data from Collier Aerospace’s HyperX allows users to tailor laminate designs to meet part specific requirements. Building on this integration, the work in this thesis expands on previous research and introduces a physics-inspired method to analyze defect interactions for AFP Laminate-Level Optimization (LLO). Using a Greedy Search (GS) algorithm coupled with a novel objective function, this approach investigates the optimization of laminate manufacturing of complex geometries, such as doubly curved surfaces. The results demonstrate the ability to produce optimal laminates, enhancing the design-build cycle efficiency and ultimately improving part quality.