A Systems Architecture Framework for AI-Integrated Smart Manufacturing Infrastructures
Keywords:
Systems Architecture, Artificial Intelligence, Smart Manufacturing, Cyber-Physical Systems, Socio-Technical Infrastructure, Industrial Governance, Industrial Internet of Things.Abstract
The convergence of artificial intelligence and industrial systems has catalyzed a fundamental shift in global production paradigms, transitioning from traditional automation to autonomous, self-organizing smart manufacturing infrastructures. This paper proposes a comprehensive systems architecture framework designed to address the multifaceted challenges of integrating large-scale AI models into industrial environments. By synthesizing principles from systems engineering, cyber-physical systems, and socio-technical theory, the framework establishes a multi-layered approach to governance, data orchestration, and operational robustness. The research emphasizes the critical trade-offs between centralized intelligence and decentralized edge computing, exploring how these structural decisions influence latency, security, and scalability. Furthermore, the paper investigates the socio-technical implications of AI integration, specifically regarding labor dynamics, human-machine collaboration, and the long-term sustainability of digitized supply chains. Through a detailed analysis of infrastructure requirements and policy considerations, this work provides a roadmap for researchers and practitioners to navigate the complexities of Industry 4.0 and beyond. The proposed framework prioritizes systemic resilience and ethical transparency, ensuring that AI-integrated manufacturing remains both economically viable and socially responsible in an era of rapid technological disruption.
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