用强化学习驱动数字孪生，实现钢铁生产智能调控。
Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production
- 基于MLOps的事件驱动架构，实时联动物理与数字孪生系统。
- 通过深度强化学习优化感应炉功率，降低能耗与废品率。
- 系统可扩展，适合多种工业场景的智能化改造。
我们提出一种基于数字孪生的智能制造方法，旨在提升钢铁生产过程的可持续性、效率与成本效益。系统依托微服务边缘计算平台，通过融合网络基础设施实时接入产线传感器数据，并构建数字孪生体。在数字孪生中部署敏捷的机器学习控制环路，优化感应炉加热过程，提升运行质量并减少工艺浪费。核心在于采用基于深度强化学习的智能体，在MLOps驱动下自主关联系统状态与数字孪生，识别最优校正策略以优化工厂功率设置。本文阐述了该方法的理论基础、架构细节及实际应用价值，展示了其在降低制造废品和提升产品质量方面的潜力。系统设计具备灵活性，其可扩展的事件驱动架构可适配多种工业应用。本研究标志着传统流程向智能系统转型的关键一步，契合可持续发展目标，凸显MLOps在数据驱动制造中的核心作用。
原文摘要 · Abstract (English)
We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant. Our system is based on a micro-service edge-compute platform that ingests real-time sensor data from the process into a digital twin over a converged network infrastructure. We implement agile machine learning-based control loops in the digital twin to optimize induction furnace heating, enhance operational quality, and reduce process waste. Key to our approach is a Deep Reinforcement learning-based agent used in our machine learning operation (MLOps) driven system to autonomously correlate the system state with its digital twin to identify correction actions that aim to optimize power settings for the plant. We present the theoretical basis, architectural details, and practical implications of our approach to reduce manufacturing waste and increase production quality. We design the system for flexibility so that our scalable event-driven architecture can be adapted to various industrial applications. With this research, we propose a pivotal step towards the transformation of traditional processes into intelligent systems, aligning with sustainability goals and emphasizing the role of MLOps in shaping the future of data-driven manufacturing.
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