mlsanityData to 14 Oct 2026

Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production

arXiv 2511.17632 ↗First posted 19 Nov 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 37 references that are in the map. First posted on arXiv 19 Nov 2025.

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5 of 37 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Deep Reinforcement Learning: An Overviewcited by 333
  3. 2023Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimizationcited by 8
  4. 2022Using Deep Reinforcement Learning for Zero Defect Smart Forgingcited by 1
  5. 2023Automated and Systematic Digital Twins Testing for Industrial Processescited by 1

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  1. 2026Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control
  2. 2026Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
  3. 2025Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
  4. 2024Machine learning for industrial sensing and control: A survey and practical perspective
  5. 2026Process-Knowledge-Embedded Safe DRL for Real-Time Dispatch of Process Loads in Industrial Microgrids
  6. 2020The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line ↗
  7. 2026Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground
  8. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives