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Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control
Manufacturing AI systems must autonomously adapt to continuous distributional shift from raw-material variability, ambient changes, and equipment aging, under strict safeguard and operator-trust requirements where model failures risk physical damage. This paper presents a closed-loop Cyber-Physical System (CPS) for autonomous model lifecycle management in automotive manufacturing, deployed since 2023. The system manages product-specialized model pairs: a sequence-to-sequence physics model (LPP) serving as a digital twin, and a deep Reinforcement Learning (RL) control policy (LCP) trained again
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-19T02:27:10.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.