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

TL;DR

This paper presents a digital twin and MLOps-enabled event-driven architecture for smart steel manufacturing, utilizing deep reinforcement learning to optimize processes, reduce waste, and improve efficiency and sustainability.

Contribution

It introduces a scalable, flexible system integrating digital twins, real-time sensor data, and reinforcement learning for autonomous process optimization in manufacturing.

Findings

Reduced manufacturing waste and increased production quality.

Demonstrated the effectiveness of reinforcement learning in process control.

Proposed a scalable architecture adaptable to various industrial applications.

Abstract

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.