Digital Transformation for Cost-Effective Maintenance

This paper investigates the application of digital transformation strategies to enhance the cost-effectiveness of maintenance practices for urban air mobility vehicles. The team has developed a digital transformation approach that combines years of experience in data collection, analysis and algorithm development along with the advancement of technologies in digital twins, machine learning and artificial intelligence. This analysis is primarily focused on structural maintenance and thermoplastic carbon fiber composites. The experimental procedure section provides a detailed examination of the specimens, testing procedures, and the obtained results. The modeling and simulation section dives into key aspects, algorithms and models utilized, and the validation process employed. The research outcomes are effectively communicated through an intuitive dashboard, emphasizing optimal data presentation and user interaction. The iterative exploration of modeling and simulation ensures a robust analysis, contributing to the advancement of efficient maintenance practices in the rapidly evolving landscape of urban air mobility.