Cloud Manufacturing enables the integration of geographically distributed manufacturing resources through advanced Cloud Computing and IoT technologies. This paradigm promotes the development of scalable and adaptable production systems. However, existing frameworks face challenges related to scalability, resource orchestration, and data security, particularly in rapidly evolving decentralized manufacturing settings. This study presents a novel nine-layer architecture designed specifically to address these issues. Central to this framework is the use of Apache Kafka for robust, high-throughput data ingestion, and Apache Spark Streaming to enhance real-time data processing. This framework is underpinned by a microservice-based architecture that ensures a high scalability and reduced latency. Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. Key components, such as RabbitMQ, contribute to low-latency performance, whereas Kafka ensures data durability and supports real-time application. Additionally, the in-memory data processing of Spark Streaming enables rapid and dynamic data analysis, yielding actionable insights. The experimental results highlight the potential of the framework to enhance operational efficiency, resource utilization, and data security, offering a resilient solution suited to the demands of modern industrial applications. This study underscores the contribution of the framework to advancing Cloud Manufacturing by providing detailed insights into its performance, scalability, and applicability to contemporary manufacturing ecosystems.

The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field.

Fused Filament Fabrication (FFF) plays a critical role in several application fields due to its affordability and manufacturing versatility. However, FFF reliability remains vulnerable to rapid environmental and operational variations, which directly influence the dimensional precision and mechanical properties of printed parts. To address these challenges, this study presents a simulation-based computational framework for the real-time early-warning supervision of FFF systems. The proposed multilayer architecture integrates high-throughput data acquisition, distributed computing, and dynamic analysis to proactively detect deviations from optimal conditions. Architectural verification follows a simulation-first methodology designed to replicate the operational dynamics of standard FFF hardware. By employing telemetry streams to test the decision-making pipeline, the study isolates computational performance, such as throughput and latency, from the confounding variables of physical hardware. This approach enables a precise, deterministic assessment of the system’s responsiveness, serving as a foundational de-risking step prior to empirical implementation. Numerical results of this study show that the integrated distributed computing model successfully manages high-frequency telemetry with a response time within the operational safety margins, confirming the architectural viability of the proposed solution. By providing insights into system behavior prior to physical deployment, this simulation-first strategy mitigates implementation risks and offers practical guidance for developing autonomous additive manufacturing workflows, advancing the transition toward intelligent industrial FFF.