Smart Manufacturing System Architecture with Intelligent Convergence: Technological Evolution and Implementation Pathways

Abstract

As the global manufacturing industry accelerates its digital and intelligent transformation, the deep integration of Artificial Intelligence (AI) with the Internet of Things (IoT) is emerging as a central driver behind the evolution of smart manufacturing systems. This paper provides a comprehensive review of the technical architecture, core technologies, and typical application scenarios of smart manufacturing within the context of this integration. From a technological perspective, it examines the transition from convolutional neural networks to vision transformers in industrial quality inspection, and explores how large language models enhance manufacturing interaction and test automation by serving as industrial Copilots, knowledge base question-answering systems, and code generators. The study also highlights the foundational role of IoT in data acquisition, real-time transmission, and intelligent analytics. In terms of applications, it demonstrates productivity improvements and modeling innovations across areas such as predictive maintenance, intelligent quality inspection, production scheduling, and smart warehousing. Despite notable progress, several challenges persist, including data silos, insufficient interpretability of models, difficulties in integrating operational technology with information technology, and a lack of interdisciplinary talent. Future research should prioritize strengthening data infrastructure, optimizing model architectures, advancing system integration and ecosystem development, and facilitating the shift of smart manufacturing from specialized tools to universal platforms, thereby offering stronger support for the intelligent advancement of the manufacturing industry.