A CPS-Based Simulation Platform for Long Production Factories

Iannino, Vincenzo; Colla, Valentina; Denker, Joachim; Göttsche, Marc

doi:10.3390/met9101025

Cited by 19 publications

(9 citation statements)

References 21 publications

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“…CyberPOS introduces simulation and verification tools as well as a new IT framework for establishing the feasibility, safety, and benefits of CPPS (Cyber Physical Production System) in the framework of "Steel Industry 4.0 Automation". Moreover, a CPS-based platform for facilities producing long steel products has been developed [58]. PRESED proposed a solution built around Big Data, Feature Extraction, ML, Analytics Server, and Knowledge Management in order to automatically analyze the sensorial time series data.…”

Section: Past and Ongoing Research Activities Funded By The Research mentioning

confidence: 99%

“…In SOProd Objected-Oriented Programming (OOP), Python language, LabView, MongoDB, and Optical character recognition are some of the technologies adopted to improve product intelligence and autonomous machine-machine and product-machine communication [69]. In this project, a de-central optimization considering a detailed product and process knowledge facilitates a process self-optimization by using individual product properties and processing information of neighboring processes [58]. Advanced methods for process monitoring and control through multi-criteria approach of performances indicators, together with optimization approaches, have been exploited in OptiScrapManage.…”

Section: Past and Ongoing Research Activities Funded By The Research mentioning

confidence: 99%

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The Challenge of Digitalization in the Steel Sector

Branca

Fornai

Colla

et al. 2020

Metals

Self Cite

119

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Digitalization represents a paramount process started some decades ago, but which received a strong acceleration by Industry 4.0 and now directly impacts all the process and manufacturing sectors. It is expected to allow the European industry to increase its production efficiency and its sustainability. In particular, in the energy-intensive industries, such as the steel industry, digitalization concerns the application of the related technologies to the production processes, focusing on two main often overlapping directions: Advanced tools for the optimization of the production chain and specific technologies for low-carbon and sustainable production. Furthermore, the rapid evolution of the technologies in the steel sector require the continuous update of the skills of the industrial workforce. The present review paper, resulting from a recent study developed inside a Blueprint European project, introduces the context of digitalization and some important definitions in both the European industry and the European iron and steel sector. The current technological transformation is depicted, and the main developments funded by European Research Programs are analyzed. Moreover, the impact of digitalization on the steel industry workforce are considered together with the foreseen economic developments.

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Section: Past and Ongoing Research Activities Funded By The Research mentioning

confidence: 99%

Section: Past and Ongoing Research Activities Funded By The Research mentioning

confidence: 99%

The Challenge of Digitalization in the Steel Sector

Branca

Fornai

Colla

et al. 2020

Metals

Self Cite

119

View full text Add to dashboard Cite

show abstract

“…In manufacturing processes, simulation has mainly been applied to adjust the process schedule or visualize the workload balance of production lines [12][13][14]. Recently, however, the interest in CPS-linked simulation has increased [15][16][17]. In the garment industry, simulation is used for various purposes such as the analysis of sewing waiting time, production capacity, and line balancing considering the state of the line [18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Jung

Park²,

Lee³

et al. 2021

Journal of Computing and Information Science in Engineering

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Implementing digital transformation in the garment industry is very difficult, owing to its labor-intensive structural characteristics. Further, the productivity of a garment production system is considerably influenced by a combination of processes and operators. This study proposes a simulation-based hybrid optimization method to maximize the productivity of a garment production line. The simulation reflects the actual site characteristics, i.e., process and operator level indices, and the optimization process reflects constraints based on expert knowledge. The optimization process derives an optimal operator sequence through a genetic algorithm, and sequentially removes bottlenecks through workload analysis based on the results. The proposed simulation optimization method improved productivity by ∼67.4%, which is 52.3% higher than that obtained by the existing meta-heuristic algorithm. The correlation between workload and production was verified by analyzing the workload change trends. This study holds significance because it presents a new simulation-based optimization model that further applies the workload distribution method by eliminating bottlenecks and digitizing garment production lines.

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“…A further RFCS project entitled "Economic and flexible decentral self-optimising production (SOPROD)" focused on the development of a distributed agent-based system platform aimed at increasing flexibility, resource efficiency and productivity as well as decreasing energy consumption of the steel production. This project inspired further projects such as the RFCS project Cyber-POS (Virtual Design of Cyber-Physical Production Optimization Systems for Long Production Factories), which was focused on establishing the feasibility, safety, and benefits of Cyber-Physical Production Systems (CPPS) through different software technologies including agents [26]. Moreover, in the project MAGUS (Manufacturing Execution System Augmented Solution) funded by Regione Piemonte (Italy) and EU, where a MAS was developed to the aim of providing a technological solution to the specific needs of the process industry [27].…”

Section: Two Exemplar Applications Of Multi-agent Systems In Steel Pr...mentioning

confidence: 99%

Multi-agent systems to improve efficiency in steelworks

Iannino

Colla

Mocci

et al. 2021

Matériaux & Techniques

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A multi-agent system consists of several computational entities capable of autonomous actions, called agents, which communicate with each other, and have the ability to coordinate their actions and to cooperate. Multi-agent systems received a great interest and attention over time, as they can be seen as a key enabling technology for complex applications, where distributed and processing of data, autonomy, and high degree of interactions in dynamic environments are required at the same time. Therefore, in view of current and future developments of the digitalization of industrial production cycles promoted by Industry 4.0, multi-agent systems are foreseen to play an increasing role for industrial production management and optimization. Because of barriers represented by large presence of legacy systems, in the steel sector agent-based technology is not widely applied yet, and multi-agent systems applications are very few. On the other hand, steel manufacturing industries are complex and dynamic systems whose production processes held a strategic role in the global economy. During last decades, the steel sector has undergone relevant transformations, especially through the massive digitalization and the innovation introduced by Industry 4.0. A further evolution is foreseen in the incoming years to improve the sustainability of the production cycle by improving energy and resource efficiency. Therefore, steel industries must face several challenges on the path toward the factory of the future. In such context multi-agent systems, through their intrinsic properties, such as autonomy, social abilities, reactivity, proactivity, and mobility, can overcome existing drawbacks and barriers, by increasing flexibility, improving resources efficiency, handling production operations, reacting to unpredicted events, optimizing production processes, and supporting legacy systems. In this paper, some applications of multi-agent systems in steel sector are presented to show the advantages and opportunities of agent-based technology.

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A CPS-Based Simulation Platform for Long Production Factories

Cited by 19 publications

References 21 publications

The Challenge of Digitalization in the Steel Sector

The Challenge of Digitalization in the Steel Sector

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Multi-agent systems to improve efficiency in steelworks

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