Adaptive Intelligent Manufacturing Control Systems

Yerofeyev, Sergey; Ipatov, Oleg; Markov, Sergey; Potekhin, V. V.; Sulerova, Angelina; Shkodyrev, Viacheslav

doi:10.2507/26th.daaam.proceedings.143

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…• Distributed computing management system parses tasks on huge number of subtasks and sends them to specified evaluation nodes [7]. Then it collects the results.…”

Section: Distributed Computing and Storage Layermentioning

confidence: 99%

“…This layer can be represented by the implementation of OPC (Object Linking and Embedding for Process Control) [7].…”

Section: Primary Processing/store Data Layermentioning

confidence: 99%

“…It can be represented by any DBMS(SQL, NoSQL, TSDB) for any type of data. (As an Example: for data from sensor -the Apache Cassandra can be used, but for project documentation-MongoDB, which is documentorientated) [7,8] …”

Section: Distributed Computing and Storage Layermentioning

confidence: 99%

“…4. Thus, the process of equipment monitoring and diagnostics includes various production units from the operator and the instrumentation service to analysts and experts at the level of the company's main specialists [9].…”

Section: Fig 4 Business Decision-making Process Based On Digital Twinmentioning

confidence: 99%

See 3 more Smart Citations

Main Principals and Issues of Digital Twin Development for Complex Technological Processes

Ponomarev¹,

Kudryashov²,

Popelnukha³

2017

DAAAM Proceedings

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To provide increasing of effectiveness of industrial systems with achievement of the guaranteed product quality, the manufacturing digital transformation paradigm is applied. According to Industry 4.0 concept, adaptive, client-oriented automatic control of product life cycle is the basic approach in global manufacturing digitalization. Such technologies as Big Data, Cloud Services, advanced analytic algorithms, have to be implemented to provide an effective control for complex technological processes. Therefore, different technologies operating information from Web as well as from field level and top level manufacture information systems should offer high degree of interoperability. This article represents the, so-called, digital twin concept description. The main development issues and paradigms are described, applying to oil refining industry.

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“…• Distributed computing management system parses tasks on huge number of subtasks and sends them to specified evaluation nodes [7]. Then it collects the results.…”

Section: Distributed Computing and Storage Layermentioning

confidence: 99%

“…This layer can be represented by the implementation of OPC (Object Linking and Embedding for Process Control) [7].…”

Section: Primary Processing/store Data Layermentioning

confidence: 99%

Section: Distributed Computing and Storage Layermentioning

confidence: 99%

Section: Fig 4 Business Decision-making Process Based On Digital Twinmentioning

confidence: 99%

See 2 more Smart Citations

Main Principals and Issues of Digital Twin Development for Complex Technological Processes

Ponomarev¹,

Kudryashov²,

Popelnukha³

2017

DAAAM Proceedings

View full text Add to dashboard Cite

show abstract

“…To realize the idea of increasing production efficiency by reduction of equipment downtime, we can implement Industry 4.0 concept and IT world technologies [2]. By using Industrial Internet of Things (IIoT) services, we can collect and store data from any devices from production line that provides comprehensive information about production state.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Cloud Services for Advance Management of Steel Transport for Continuous Casting Production

Kudriashov¹,

Протасов²,

Markov³

et al. 2016

Proceedings of the 27th International DAAAM Symposium 2016

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Nowadays, manufacturing is on the threshold of the fourth industrial revolution. It means the increase of enterprise productivity requirements. To improve the productivity, factories have to implement advanced analytical algorithms. It means the increasing demand for big data technology and cloud services for modern manufacturing systems. Therefore, different technologies operating information from Web as well as from field level and top level manufacture information systems should offer high degree of interoperability. For instance, the implementation of Industry 4.0 concepts technologies will be presented on the example of steel transportation for continuous casting production, to access benefits unavailable before. This paper refers to the problems of Cloud Services and Big Data usage in order to enhance productivity of steel transportation system on metallurgical plant.

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