arXiv:2511.17632v1 Announce Type: new 
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.

تم تقديم نهج جديد للتصنيع الذكي يركز على نظام قائم على التوأم الرقمي لتحسين الاستدامة والكفاءة والجدوى الاقتصادية في إنتاج الصلب. يستخدم هذا النظام منصة حوسبة حافة قائمة على الخدمات الصغيرة تعالج بيانات المستشعرات في الوقت الفعلي لتحسين تسخين الأفران الحثية وتقليل الفاقد من خلال حلقات التحكم المعتمدة على التعلم الآلي.

Se ha introducido un nuevo enfoque para la fabricación inteligente, centrado en un sistema basado en gemelos digitales para mejorar la sostenibilidad, la eficiencia y la rentabilidad en la producción de acero. Este sistema utiliza una plataforma de computación en el borde basada en microservicios que procesa datos de sensores en tiempo real para optimizar el calentamiento de hornos de inducción y reducir desperdicios mediante bucles de control basados en aprendizaje automático.

Une nouvelle approche de la fabrication intelligente a été introduite, axée sur un système basé sur un jumeau numérique pour améliorer la durabilité, l'efficacité et la rentabilité dans la production d'acier. Ce système utilise une plateforme de calcul en périphérie à micro-services qui traite les données de capteurs en temps réel pour optimiser le chauffage des fours à induction et réduire les déchets grâce à des boucles de contrôle basées sur l'apprentissage automatique.

A new approach to Smart Manufacturing has been introduced, focusing on a Digital Twin-Based system to enhance sustainability, efficiency, and cost-effectiveness in steel production. This system utilizes a micro-service edge-compute platform that processes real-time sensor data to optimize induction furnace heating and reduce waste through machine learning-based control loops.

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

arXiv:2512.03891v1 Announce Type: cross 
Abstract: Active suspension systems are critical for enhancing vehicle comfort, safety, and stability, yet their performance is often limited by fixed hardware designs and control strategies that cannot adapt to uncertain and dynamic operating conditions. Recent advances in digital twins (DTs) and deep reinforcement learning (DRL) offer new opportunities for real-time, data-driven optimization across a vehicle's lifecycle. However, integrating these technologies into a unified framework remains an open challenge. This work presents a DT-based control co-design (CCD) framework for full-vehicle active suspensions using multi-generation design concepts. By integrating automatic differentiation into DRL, we jointly optimize physical suspension components and control policies under varying driver behaviors and environmental uncertainties. DRL also addresses the challenge of partial observability, where only limited states can be sensed and fed back to the controller, by learning optimal control actions directly from available sensor information. The framework incorporates model updating with quantile learning to capture data uncertainty, enabling real-time decision-making and adaptive learning from digital-physical interactions. The approach demonstrates personalized optimization of suspension systems under two distinct driving settings (mild and aggressive). Results show that the optimized systems achieve smoother trajectories and reduce control efforts by approximately 43% and 52% for mild and aggressive, respectively, while maintaining ride comfort and stability. Contributions include: developing a DT-enabled CCD framework integrating DRL and uncertainty-aware model updating for full-vehicle active suspensions, introducing a multi-generation design strategy for self-improving systems, and demonstrating personalized optimization of active suspension systems for distinct driver types.

تم تطوير إطار عمل جديد يستخدم تقنية التوأم الرقمي والتعلم العميق المعزز (DRL) لتحسين أنظمة التعليق النشطة للسيارات الكاملة. تتناول هذه الطريقة قيود أنظمة التعليق التقليدية من خلال تمكين التعديلات في الوقت الفعلي المستندة إلى البيانات، مما يعزز راحة السيارة وسلامتها واستقرارها في ظروف متنوعة.

Se ha desarrollado un nuevo marco que utiliza la tecnología Digital Twin y el Aprendizaje por Refuerzo Profundo (DRL) para optimizar las suspensiones activas de vehículos completos. Este enfoque aborda las limitaciones de los sistemas de suspensión tradicionales al permitir ajustes en tiempo real basados en datos, mejorando así el confort, la seguridad y la estabilidad del vehículo en diversas condiciones.

Un nouveau cadre utilisant la technologie Digital Twin et l'apprentissage par renforcement profond (DRL) a été développé pour optimiser les suspensions actives de véhicules complets. Cette approche répond aux limitations des systèmes de suspension traditionnels en permettant des ajustements en temps réel, basés sur des données, pour améliorer le confort, la sécurité et la stabilité du véhicule dans des conditions variées.

A new framework utilizing Digital Twin technology and Deep Reinforcement Learning (DRL) has been developed for optimizing full vehicle active suspensions. This approach addresses the limitations of traditional suspension systems by enabling real-time, data-driven adjustments to enhance vehicle comfort, safety, and stability under varying conditions.

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

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