Movable Virtual Defect Detection Training System (MOVIDDTS)

Non-Destructive Inspection (NDI) is critical for identifying issues in aircraft components before they lead to catastrophic failures. Regular inspections and maintenance based on NDI findings are essential to ensure the safety and airworthiness of aircraft. Regulatory authorities like the Federal Aviation Administration (FAA), the U.S. Air Force, and others have strict guidelines and requirements for NDI procedures to ensure the safety of commercial and military aviation. NDI refers to a range of techniques used to inspect aircraft components and structures without causing damage. The most commonly used NDI procedure used by the U.S. Air Force is Eddy Current (ET) which uses electromagnetic induction to detect surface and near-surface defects, such as cracks or corrosion. It is used to inspect aircraft structures and materials for issues like stress corrosion cracking. Mixed Reality (MR) refers to an immersive technology that combines elements of both virtual reality (VR) and augmented reality (AR) to create a new interactive environment where digital and physical objects coexist and interact. MR has several applications in the field of aircraft maintenance, offering numerous benefits for maintainers and technicians. In training, for example, MR can be used to create realistic training scenarios where maintainers can practice repairing and maintaining aircraft systems in a safe and controlled virtual environment. It allows for hands-on training without the need for actual physical aircraft, reducing the risk of damage during training. Machine Learning (ML) can be applied to identify issues with aircraft maintainer training and enhance the effectiveness of training programs in several ways including: Personalized training plan: ML models can create personalized training plans for each maintainer based on their strengths and weaknesses. This ensures that training resources are focused on improving specific skills or knowledge gaps. Performance Assessment: ML algorithms can analyze data from training exercises and simulations to assess how well maintainers are performing various tasks. This can help identify areas where individuals or groups may be struggling or excelling. Combining MR and ML to identify and address issues on aircraft, such as NDI, for training purposes is an innovative and potentially powerful approach. This convergence of technologies can enhance training, maintenance, and safety in the aviation industry. Integrating ML models with MR interface allows real-time analysis of data that can be analyzed by inspectors and students for training as well as operations. This combination can create a powerful tool for aircraft maintenance and training that enhances safety, reduces downtime, and improves the skills of aviation professionals. Itĺs essential to work closely with experts to ensure the system meets all safety and regulatory requirements. á