Open Access
ARTICLE
AI-Optimized Edge Computing Architectures: Improving Real-Time Data Processing in Smart Manufacturing Systems
Issue Vol. 1 No. 01 (2025): Volume 01 , Issue 01 Oct 2025 --- Section Articles --- Published Date: 2025-10-01
Abstract
Smart manufacturing is rapidly evolving through the convergence of automation, intelligent sensing, Industrial Internet of Things (IIoT), and collaborative robotics. However, real-time data processing remains a challenge due to network latency, massive data loads, and computational bottlenecks in centralized cloud systems. This research examines AI-optimized edge computing architectures as a transformative solution to enhance real-time responsiveness, operational continuity, predictive insights, and system reliability in manufacturing environments. AI-accelerated edge nodes—using deep learning, federated intelligence, and distributed stream analytics—enable autonomous decision-making at millisecond-level speeds directly on the factory floor. The study evaluates architectural models, algorithmic optimizations, bottleneck mitigation techniques, and hardware accelerators such as TPUs and edge GPUs. Through simulations, case studies, and comparative tables, the analysis demonstrates that AI-integrated edge systems significantly improve throughput, reduce latency, enhance security, and optimize performance in high-variety, high-volume production systems. This paper establishes a detailed framework for implementing AI-driven edge infrastructure in next-generation smart factories.
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