The transition from Industry 4.0 to Industry 5.0 emphasizes human-centric, sustainable, and intelligent manufacturing by integrating human expertise with advanced technologies such as Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), Digital Twins (DT), Edge Computing, Cloud Computing, Collaborative Robots (Cobots), and Explainable AI (XAI). This paper proposes an Intelligent Human-Centric Cyber-Physical System (HC-CPS) framework comprising six interconnected layers for real-time monitoring, predictive maintenance, adaptive production scheduling, quality optimization, and human-centered decision support. A multi-objective optimization model and AI-driven closed-loop decision-making algorithm enhance production efficiency, equipment reliability, energy utilization, product quality, and human–machine collaboration while reducing downtime and operational costs. Human operators remain actively involved through collaborative interfaces that validate or modify AI recommendations. Comparative evaluation demonstrates significant improvements over conventional Industry 4.0 systems in Overall Equipment Effectiveness (OEE), predictive maintenance, operational safety, and manufacturing flexibility, providing a scalable, resilient, and sustainable foundation for future Industry 5.0 smart factories.
Andrey Ershov, Alexey Lyapunov· International Journal of Mod...· 0 citations
A Large Language Model-Augmented Machine Learning Pipeline that integrates data acquisition, intelligent preprocessing, semantic feature engineering, automated model selection, hyperparameter optimization, explainable AI, continuous monitoring, and feedback-driven refinement within a unified framework is proposed.
Andrey Ershov· International Journal of Mac...· 0 citations
This work forms a prior method based on Deep Deterministic Policy Gradient controller and a adaptive unscented Kalman filter which continuously providing constantly estimating robot states and improving the motion primitives in hazardous operating conditions to validate that end-to-end AI navigation architectures deliver the robust performance needed for next-gen automated industrial monitoring and non-destructive evaluation at scale.
Andrey Ershov, Alexey Lyapunov· International Journal of Int...· 0 citations
A Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management is proposed.
Andrey Ershov, Alexey Lyapunov· International Journal of Mod...· 0 citations
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