A Reference Architecture for Quality Improvement in Steel Production

Arnu, David; Yaqub, Edwin; Mocci, Claudio; Colla, Valentina; Neuer, Marcus J.; Fricout, Gabriel; Renard, Xavier; Mozzati, Christophe; Gallinari, Patrick

doi:10.1007/978-3-658-19287-7_12

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…Another interesting approach that leads to increasing productivity by enhancement of product intelligence, is mentioned in [16]. In [17], an architecture for storing and processing massive amounts of data that are created during steel manufacturing with the aim of improving the quality of steel production processes is presented.…”

Section: Introductionmentioning

confidence: 99%

A CPS-Based Simulation Platform for Long Production Factories

Iannino

Colla

Denker³

et al. 2019

Metals

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Production technology in European steel industry has reached such a level, that significant improvements can only be reached by through process optimization strategies instead of separately improving each process step. Therefore, the connection of suitable technological models to describe process and product behavior, methods to find solutions for typical multi-criterial decisions, and a strong communication between involved plants is necessary. In this work, a virtual simulation platform for the design of cyber-physical production optimization systems for long production facilities focusing on thermal evolution and related material quality is presented. Models for describing physical processes, computers, software and networks as well as methods and algorithms for through process optimization were implemented and merged into a new and comprehensive model-based software architecture. Object-oriented languages are addressed and used because they provide modularity, a high-level of abstraction and constructs that allow direct implementation and usage of the cyber-physical production systems concepts. Simulation results show how the proper connection between models, communication, and optimization methods allows feasibility, safety and benefits of cyber-physical production systems to be established. Furthermore, the software architecture is flexible and general and thus, can be transferred to any steel production line as well as outside the steel industry.

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Section: Introductionmentioning

confidence: 99%