... digital twin frameworks for AE-driven prognostics of pressure vessels. It eliminates dependence on costly experiments or finite element simulations, offering a scalable, data-efficient approach suitable for Industry 4.0 applications. Aswin Karkadakattil can be contacted at: ashwinharik20000@gmail.com...

... in practical industrial environments. Research limitations/implications The framework provides a ready-to-deploy decision-support tool for energy-intensive manufacturing sectors, reducing downtime, improving energy efficiency and enabling broader implementation of Industry 4.0 strategies. Practical...

... (IMES) effectively addresses emerging issues due to market fluctuations, regulatory changes and sustainability objectives, particularly by leveraging breakthroughs in automation, reconfigurability and real-time decision-making to achieve Industry 4.0 aspirations. The primary inquiry is how the food...

... the first to integrate federated learning, reinforcement learning and privacy-preserving AI techniques for secure human–robot collaboration in Industry 4.0. Unlike conventional AI models that rely on centralized data processing, the proposed MARL-FL framework enables secure, decentralized learning, reducing...