Date of Award
Open Access Dissertation
Advanced composite materials came about in 1966 and have since been widely used due to the possibility of superior structural performance while also achieving weight reductions. Such opportunities have led to composite materials being used to fabricate complex components, often in the aerospace sector. Most components, especially in aviation, are on a large scale and are outside the capabilities of traditional composite manufacturing techniques. Traditional manufacturing methods are also labor intensive, time consuming, have a high level of material scrap, and are prone to human error. This has led to the need for innovative manufacturing solutions to withstand the ever-increasing throughput requirement. One driving technique is Automated Fiber Placement (AFP) which is a relatively new manufacturing technique that has rapidly evolved since its commercial inception in the 1980s. AFP gives industries the ability to manufacture large parts with high speed, repeatability, and process quality. However, even with the state-of-the-art machines and process controls, the AFP process is still far from perfected.
With the advent of Industry 4.0, many manufacturing sectors have begun the exploration into the use of smart and digital manufacturing with implementation of machine learning. However, AFP manufacturing, and the remaining composite manufacturing sector, has yet to explore the philosophies of the future of manufacturing. Rather, siloed efforts have been enacted to advance each of the technical challenges associated with AFP resulting in an open loop system that is difficult to optimize on a global level. Such efforts also restrict the possibility of increased manufacturing throughput due to systems operating in suboptimal configurations. To overcome this hurdle, an integrated data flow is enacted that combines the design, process planning, manufacturing, and inspection pillars of AFP into a holistic workflow. This is enabled by employing industry level tools combined with efficient and versatile modeling techniques. The models then allow for an informed analysis that considers the combination of multiple AFP lifecycle trades. With this streamlined workflow, multiple optimization algorithms are developed to determine the globally optimal manufacturing plan to generate a structure with AFP. The combination of these methodologies into a common tool creates a comprehensive AFP process planning optimization. The modeling and optimization framework is applied to a doubly curved tool surface case study. Results demonstrate the effectiveness of the presented framework in terms of manufacturing defect reduction and process efficiency.
Brasington, A. R.(2023). Comprehensive Process Planning Optimization Framework for Automated Fiber Placement. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/7304