Keywords
AI Governance
Abstract
The deployment of artificial intelligence in safety-critical, economically significant manufacturing environments raises engineering and governance challenges that extend far beyond the benchmark accuracy metrics that dominate AI research publications. Real-world manufacturing AI systems must operate reliably under distribution shift, communicate their uncertainty to human operators in interpretable forms, comply with an evolving landscape of AI-specific regulations, and earn the calibrated trust of the engineers, operators, and managers who rely on them. This review provides a comprehensive synthesis of AI engineering and governance for smart manufacturing, examining uncertainty quantification and reliability engineering for manufacturing AI, Bayesian deep learning and probabilistic AI for industrial decision support, human-centered AI design and trust calibration in manufacturing, responsible AI principles and governance frameworks, regulatory compliance (EU AI Act, ISO standards, sector-specific mandates), and the integration of these engineering and governance concerns with the four preceding Yi Bao AI frameworks (RL-MPC, Adaptive Manipulation, Quality Intelligence Architecture, and Neuromorphic Industrial Intelligence Architecture). We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how uncertainty-aware sensing and human-centered visualization enhance the trustworthiness of intelligent manufacturing systems. A central contribution is the articulation of an integrated Responsible Manufacturing AI Lifecycle (RMAL) framework that unifies uncertainty quantification, human-centered AI design, governance mechanisms, and regulatory compliance throughout the entire lifecycle of AI systems in manufacturing.
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