Developing a Holistic Automated Fiber Placement Digital Engineering Solution (HADES)

Manufacturing demand for composite materials has rapidly increased over the past decades. Key technologies, such as Automated Fiber Placement (AFP), have met manufacturing demands in terms of geometric complexity and throughput, however there are significant technology gaps related to process planning, controlling the manufacturing process in-situ, and understanding the influence of process parameters on part behavior. These challenges drive the need for increased operator involvement, which slows the process. A significant number of process models have been developed to help improve the understanding and operation of the AFP process, but the ability to use these insights to adapt process instructions based on in-situ data is lacking. This approach does not provide a full understanding of the process and delivers small, insufficient improvements. Integer Technologies and the University of Delaware (UDEL) propose a Holistic Automated fiber placement Digital Engineering Solution (HADES), a software suite that will couple physics-based and data driven digital twins with in-situ data collection to provide AFP process stakeholders with the information they need to plan an optimized process and reduce the chances of defects and re-work.ÝInteger Technologies experience with digital twins, digital engineering frameworks, and advanced manufacturing is supplemented with the University of Delawareís expertise in AFP modelling and operation. The solution is comprised of a modular modelling framework that houses numerous physics-based and data-driven process models. These process models are integrated within the framework to create digital twins that match the configuration of a physical machine or manufacturing process. This integration produces a synthetic test environment that enables virtual testing of process plans and interaction with the physical machine to provide optimization and real-time process feedback. The design of the modular modelling framework enables the ability to customize the testing environment to enable application to a broad range of AFP machine configurations.