A cyber-physical architecture based on the IoT, which uses machine intelligence to monitor, analyze, and control manufacturing systems in real-time, which can be both scaled and powerful to serve next-generation intelligent manufacturing systems.
Abstract
Abstract. CPMS have become a major facilitator of smart factories through a combination of physical operations and the computational intelligence and real-time communication. Combination of Internet of Things (IoT) technologies and machine intelligence offers novel possibilities to realize adaptive, efficient and autonomous manufacturing processes. Nevertheless, the co-ordination of the physical and cyber layer is a serious issue because of the dynamic characteristic of industrial set ups and the high amount of heterogeneous sensor data. This paper proposes a cyber-physical architecture based on the IoT, which uses machine intelligence to monitor, analyze, and control manufacturing systems in real-time. The approach that is proposed is based on sensor-driven data acquisition, intelligent processing, and adaptive decision-making to optimize system performance. To reduce the production inefficiency, energy usage as well as fault occurrence, a multi-objective formulation is created in the context of keeping operational constraints. The structure is tested on a realistic manufacturing case based on real-time data input. Findings indicate the improvement of operational efficiency, the capability of fault detection and the response time is reduced, compared to the traditional methods. The methodology suggested can be both scaled and powerful to serve next-generation intelligent manufacturing systems.
Detailed analysis of smart manufacturing systems that utilize the IoT and automation technology indicates a high improvement in operational performance, predictive accuracy, and decision-making ability when compared to the traditional manufacturing system.
Aiko Yamamoto· International Journal of Mod...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges and underscores the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Asmarani Ahmad Puzi, Ahmad Anwar Zainuddin, Muhammad Afham Anuar et al.· International Journal of Inn...· 0 citations
Accurate production has been facilitated as a pillar in the contemporary industrialization, prompted by the growing desire of quality products, decreasing production expenses, minimal wastes, and accelerated time-to-market. The technology behind Internet of Things (IoT) has converged with Big Data analytics which has drastically changed conventional manufacturing paradigms that have permitted real time monitoring, intelligent decision-making and predictive control of manufacturing processes. IoT makes it easy to pervasively sense and interconnect machines, tools, products and human operators, creating large volumes of heterogeneous data from the manufacturing lifecycle. The computation and analytical resources needed to store, process and extract actionable information out of this data are available through the use of big data technologies. The following paper demonstrates an in-depth study of the methods of integrating IoT and Big Data to achieve precision manufacturing. It examines system architecture, data acquisition, architecture, analytics, and decision-support models that promote accuracy and efficiency in the processes and quality in the products. An extensive literature review identifies areas of recent innovations and research lag in the field of smart manufacturing, Industrial IoT (IIoT) and data-driven manufacturing. The suggested methodology gives a description of an end-to-end system that includes the deployment of sensors, data ingestion, data preprocessing, analytics, and feedback control. The experimental findings and discussion illustrate how the Big Data analytics based on IoTs enhance predictive maintenance, quality assurance and optimization processes. The paper also ends with a note on the major challenges, research directions and the use of new technologies like artificial intelligence and digital twins in enhancing precision manufacturing.
Liam Walker, Grace Young· International Journal of Mod...· 0 citations
Industrial Internet of Things (IIoT) deployments generate high-volume operational data, yet many manufacturing systems still use this data mainly for monitoring rather than closed-loop decision support. This paper presents an integrated IIoT-optimization framework for data-driven smart manufacturing. The contribution is positioned as a conceptual and simulation-based architecture rather than a fully validated industrial deployment. The framework contains four layers: physical data acquisition, analytics and processing, optimization and decision support, and presentation/application feedback. Its Adaptive Model Integrator (AMI) is implemented as a context-aware selection policy that chooses among mathematical programming, heuristic dispatching, multi-objective optimization, model predictive control, and metaheuristic solvers according to data quality, system stability, problem structure, active objectives, and available computation time. A 720-hour synthetic MATLAB simulation of a five-line, 30-machine production system with 240 virtual sensors is used to illustrate the framework. Under the stated assumptions, the AMI-enabled configuration improves simulated throughput from 852.2 to 961.0 units/hour, reduces energy consumption from 1.198 to 1.128 kWh/unit, increases quality rate from 94.46% to 95.75%, and raises OEE from 77.39% to 84.75%. These results are reported as preliminary simulation outcomes, not as evidence of field-level industrial benefit. The paper also specifies the simulation assumptions, baseline logic, AMI decision rules, limitations, and future validation requirements.
Sami Gazem Abdullah Thabet, Mohammed Baggash· 2026 6th International Confe...· 0 citations
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