This paper will examine a methodology to enable the usage of Human Digital Twins (HDTs) for Quality Assurance in the aerospace manufacturing domain. Common-place hardware and infrastructure, including cloud-based facility security cameras, cloud-based commercial virtual environments, a virtual reality (VR) headset, and artificial intelligence (AI) detection algorithms, have been connected via application programming interfaces (API) to enable a 24-h surveillance and feedback capability for a representative aerospace manufacturing cell. Human operators who perform defined manufacturing assembly operations in real life in the cell can utilize this methodology to digitize their performance and provide objective evidence of conformity and safety messaging for their human-centric manufacturing operation in real time. The digitization of real human-centric performance using this methodology creates the foundation for a HDT. This paper will present the application of HDTs in a manner that can easily be scaled across manufacturing operations while utilizing technologies that are already commonly inserted into existing manufacturing operations, which facilitates the exploration of HDT concepts without the need for expensive capital purchases and emerging technologies.

Inspection of tube networks is an important process in the manufacturing and maintenance of aircraft. Currently, borescopes are commonly used with limited success. To improve the inspection process, a prototype design based on a vertebra concept is introduced, analyzed, and optimized. The modular robotic inspection system (MoRIS) allows for a microsized camera to be driven noninvasively through a 3-m-long, 36-mm internal diameter tube network. MoRIS provides a complete view of the tube walls. Our system includes a localization algorithm that tracks the progress of the robot inside the tubes. This algorithm results in an experimentally validated error of 19.7 mm. Using MoRIS, operators can inspect tube networks that previously required hazardous disassembly of aircraft.

Foreign object debris (FOd) in rivet holes, machined holes, and fastener sites poses a critical risk to aerospace manufacturing, where current inspections rely on manual visual checks with flashlights and mirrors. These methods are slow, fatiguing, and prone to error. This work introduces HANNDI, a compact handheld inspection device that integrates controlled optics, illumination, and onboard deep learning for rapid and reliable inspection directly on the factory floor. The system performs focal sweeps, aligns and fuses the images into an all-in-focus representation, and applies a dual CNN pipeline based on the YOLO architecture: one network detects and localizes holes, while the other classifies debris. All training images were collected with the prototype, ensuring consistent geometry and lighting. On a withheld test set from a proprietary ≈3700 image dataset of aerospace assets, HANNDI achieved per-class precision and recall near 95%. An end-to-end demonstration on representative aircraft parts yielded an effective task time of 13.6 s per hole. To our knowledge, this is the first handheld automated optical inspection system that combines mechanical enforcement of imaging geometry, controlled illumination, and embedded CNN inference, providing a practical path toward robust factory floor deployment.

Foreign Objects (FO) within an aircraft fuel tank present a serious risk of damage and possible catastrophic loss of the aircraft. As such, aircraft manufacturers place great emphasis on the thorough inspection of fuel tanks before they are closed and sealed. If a FO was left undetected in the tank then it could migrate throughout the fuel system due to phenomenon such as aircraft maneuvers and fuel sloshing and could potentially clog filters and pumps, block fuel tubes, or create structural damage which could harm the integrity of the aircraft. While industry carefully develops the physical inspection techniques for the fuel tank and implements preventive actions to mitigate the entry of FO into the tank during manufacture, there is no documented analysis about how the FO could move throughout the fuel system. In fact, current computational fluid dynamic (CFD) analysis of fuel systems is built on the assumption of FO-free fluid, and finite element methods (FEM) treat the fuel system as a solid body. This paper will propose a representative aircraft fuel tank design and create a methodology for evaluation of FO migration within that fuel tank. The methodology will include physical experimentation of a fuel tank prototype and documentation of FO migration characteristics for distinct types of FO and present the FO migration results expressed in terms of risk. Additionally, a simulation approach will be created to analyze not only fluid flow, but also the movement of a FO within that fluid flow. These results will enable a new understanding of how FO impacts an aircraft fuel system which can be useful for future FO-resilient fuel tank design and safer aircraft operation.

Material delivery plays a critical role in manufacturing efficiency, with manual retrieval introducing non-value-added (NVA) time and disrupting workflow continuity. Autonomous mobile robots (AMRs) can improve performance by enabling overlap between material transport and productive work, but their effectiveness depends on how they are deployed. In this work, a convolutional neural network (CNN)-based autonomous dispatch framework was implemented and tested in a controlled experimental setting. This study utilized a representative aerospace assembly task to evaluate three material delivery approaches across 60 runs, including manual walking, manual AMR dispatch, and autonomous AMR deployment. System performance was assessed using total operation time, panel lead times, and non-value-added time. Results showed that manual AMR dispatch significantly increased total operation time and non-value-added time due to sequential task execution. Autonomous deployment reduced this inefficiency by enabling preemptive material transport and overlap with operator activity, but did not significantly outperform manual walking under the tested conditions. Operator variability also influenced non-value-added time under automated dispatch. These results indicate that AMR effectiveness depends strongly on deployment timing and workflow synchronization, with the greatest potential benefits expected in environments that allow greater overlap between transport and productive work.