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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.

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.