2023· International Journal of Intelligent Automation & Robotics Engineering· 0 citations
TL;DR
This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization, and establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories.
Abstract
The rapid advancement of Industry 4.0 has accelerated the adoption of intelligent automation technologies that enhance productivity, flexibility, quality, and operational resilience in manufacturing. Cyber-Physical Production Systems (CPPS) have emerged as a key enabler of smart manufacturing by integrating physical production equipment with Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud and edge computing, digital twins, and autonomous control. Unlike traditional centralized automation, CPPS enables decentralized communication, real-time data exchange, intelligent decision-making, and adaptive production control. Autonomous factory automation allows machines, robots, and sensors to monitor operating conditions, predict equipment failures, and perform corrective actions with minimal human intervention. AI techniques such as machine learning, deep learning, reinforcement learning, and predictive analytics optimize production scheduling, resource allocation, predictive maintenance, and operational efficiency. Meanwhile, IIoT sensors, edge computing, and cloud platforms provide continuous monitoring, low-latency processing, and enterprise-level data analytics.This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization. The framework enables manufacturing systems to dynamically respond to equipment failures, production disturbances, and changing customer demands while maintaining operational stability and product quality. Mathematical models for production optimization and resource allocation demonstrate improvements in throughput, equipment utilization, energy efficiency, and production time. Overall, the proposed framework establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories, supporting next-generation smart manufacturing through the seamless integration of AI, IIoT, and cyber-physical engineering.
The rapid advancement of Industry 4.0 has transformed conventional manufacturing into intelligent smart factories by integrating Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, and artificial intelligence (AI). As manufacturing environments become increasingly complex, traditional human-driven decision-making is insufficient for real-time production optimization. Autonomous Decision Support Systems (ADSS) address this challenge by combining AI, machine learning, digital twins, edge computing, and predictive analytics to enable intelligent, data-driven decision-making with minimal human intervention. This paper presents a scalable ADSS framework that integrates IIoT, edge-cloud computing, and digital twin technology for real-time monitoring, predictive maintenance, dynamic scheduling, and autonomous production optimization. The proposed architecture includes data acquisition, preprocessing, feature engineering, predictive analytics, decision optimization, autonomous execution, and continuous learning. Reinforcement learning and explainable AI improve decision accuracy, adaptability, and transparency, while federated learning enhances data privacy and reduces communication latency. Experimental results demonstrate significant improvements in production efficiency, equipment utilization, predictive maintenance, energy efficiency, quality control, and manufacturing responsiveness compared to conventional decision support systems. The proposed framework provides a scalable foundation for Industry 5.0, enabling sustainable, resilient, and intelligent manufacturing through seamless collaboration between human expertise and autonomous AI systems.
Jose Fernandez, Marta Silva· International Journal of Int...· 0 citations
Industry 4.0 integrates Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud computing, edge computing, and cyber-physical systems to enable intelligent and automated manufacturing. Unlike traditional rule-based automation, AI-driven process optimization enables predictive decision-making, adaptive control, and continuous learning in dynamic production environments. This paper proposes an AI-enabled process optimization framework that combines real-time sensor data, predictive analytics, machine learning, reinforcement learning, optimization algorithms, and closed-loop feedback to improve manufacturing performance. The framework predicts equipment failures, detects process anomalies, optimizes production schedules, enhances resource utilization, and reduces energy consumption. Performance is evaluated using metrics such as production efficiency, cycle time, defect rate, machine utilization, predictive maintenance accuracy, throughput, energy efficiency, and operational cost. The proposed framework provides a scalable and intelligent solution for Industry 4.0 and Industry 5.0 manufacturing, improving productivity, sustainability, operational resilience, and decision-making in automated production systems.
N. Wirth· International Journal of Int...· 0 citations
The rapid evolution of Industry 5.0 has accelerated the integration of intelligent automation, artificial intelligence (AI), Industrial Internet of Things (IIoT), and cyber-physical systems into modern manufacturing environments. One of these newly developed technologies, DT technology has recently emerged as one of the transformational paradigms in realising predictive intelligence, autonomous maintenance and online operational optimisation of robotic systems. Conventional robotic maintenance strategies, such as corrective and preventive maintenance often lead to unexpected downtimes, unnecessary resources allocation and inflated maintenance costs largely due to the nature of scheduled inspections or post-failure interventions. The maintenance frameworks enabled by Digital Twin overcome these limitations as they deterministically establish a dynamically-updated virtual representation of physical robotic assets connecting sensor networks, cloud-edge computing, AI analytics and real-time simulation. This paper proposes a full Digital Twin-based autonomous robotic maintenance framework along with data acquisition from multiple sensors, edge intelligence, machine learning (ML)-based diagnosis and prediction of failures and autonomous decision-making for predictive maintenance. The proposed framework supports continuous monitoring of health, anomaly detection, RUL prediction and adaptive maintenance scheduling with minimal operational disruptions. A comparative analysis reveals that Digital Twin-assisted maintenance not only increases fault detection precision, maintenance efficiency, system availability, and operational reliability compared to traditional practices. It further discusses the current technological challenges, research gaps, and avenues for future work relating to federated Digital Twins, explainable AI (XAI), collaborative robotics, and sustainable intelligent maintenance systems. This framework lays a strong, scalable foundation for Industry 5.0 ecosystems of next generation autonomous robotic maintenance in the smart factory.
O. Olesen· International Journal of Int...· 0 citations
The Fourth Industrial Revolution is accelerating the adoption of Industry 4.0 through intelligent computing, Industrial Internet of Things (IIoT), edge computing, and AI-driven automation. Traditional cloud-based industrial systems often experience latency, bandwidth limitations, network congestion, and privacy concerns, making them unsuitable for real-time manufacturing applications. This paper proposes an Edge Intelligence framework that integrates distributed edge computing, real-time AI analytics, and autonomous decision-making to process industrial IoT data locally. The architecture consists of four layers: perception, edge intelligence, autonomous decision, and cloud coordination. Industrial sensors, PLCs, and robotic systems collect operational data, while lightweight machine learning, deep learning, and reinforcement learning models perform feature extraction, anomaly detection, predictive analytics, and adaptive control with minimal latency. A mathematical optimization model minimizes processing delay, energy consumption, and resource utilization while maximizing accuracy and efficiency. Experimental evaluation demonstrates improved real-time performance, decision accuracy, fault detection, scalability, and resource utilization, making the framework well suited for next-generation smart manufacturing and sustainable industrial automation.
V. Sethi· International Journal of Int...· 0 citations
Cyber-Physical Systems (CPSs) are transforming Industry 4.0 by integrating computation, networking, and physical processes to enable intelligent industrial automation. However, increasing system complexity introduces challenges related to reliability, fault tolerance, cybersecurity, and maintenance. This study proposes an AI-enabled self-healing CPS framework that supports autonomous fault detection, diagnosis, prediction, and recovery. The framework combines machine learning, deep learning, digital twin technology, and reinforcement learning to continuously monitor data from industrial sensors, PLCs, robotic systems, and network devices. A closed-loop architecture comprising monitoring, analysis, decision, action, and learning enables real-time anomaly detection and automated corrective actions such as parameter optimization, workload redistribution, and system reconfiguration without interrupting operations. By continuously learning from operational data, the framework enhances predictive maintenance, improves system resilience against hardware, software, and communication failures, and increases operational efficiency. The proposed approach provides a scalable foundation for next-generation smart manufacturing systems with enhanced autonomy, reliability, adaptability, and industrial intelligence.
José María Troya, R. L. de Mántaras· International Journal of Mod...· 0 citations
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