First, automated ATL process coupled with THz-based in-process monitoring together with hybrid-twin simulation methods will be developed for eco-efficient and advance manufacturing of innovative reprocessable-repairable-recyclable (3R)-resin-and state-of-the-art thermoplastic composites. Second, innovative data- and physics-based machine learning algorithms for damage detection and location combined with advanced high-performance computing (HPC)-based multi-physics and artificial intelligent-powered digital twin tools for fatigue life prediction, will be implemented to transform information from optimized onboard piezoresistive sensors data networks interfaced with low-power wireless communication platform to health and usage assessment and prognosis. Third, augmented reality tools together with novel laser-assisted methods for surface cleaning and monitoring , smart monitoring and in-situ tailored heating of composite repair blankets will be further developed to provide additional assistance in manual scarf repair operations , increasing reliability of repair process, while supporting the modification and virtual certification of MRO practices. Thus, a novel digital twin-driven framework will be implemented into a common IIoT platform to integrate the developed models and data acquired, providing bidirectional dataflow, and enabling the implementation of a holistic and comprehensive data management methodology ensuring to adequately create, capture, share, and reuse knowledge along the entire aircraft lifecycle.