This study presents an AI model management method for maintaining the predictive performance of virtual metrology (VM) systems in steel manufacturing processes. The proposed method addresses concept drift (CD), a major threat to long-term model reliability, by orchestrating a dual-update strategy that autonomously revises both the feature store and the data store. Unlike conventional model maintenance approaches based on static inputs and accumulated data, our framework continuously reassesses feature relevance and adapts retraining datasets to reflect evolving process conditions. Applied to a real-world steel sintering process, the method achieved significant recovery in predictive accuracy—from a post-deployment drop of 65% to over 93% after updates. This study highlights the importance of proactive maintenance for AI models and demonstrates how an intelligent update strategy can extend the usable life and reliability of VM systems in critical production workflows. The findings contribute to the broader field of AI-enabled asset management by offering a scalable solution for condition monitoring, retraining scheduling, and long-term performance assurance.
Seo et al. (Thu,) studied this question.