The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms.
Bouhala et al. (Mon,) studied this question.