Analysis of Control Architectures in the Context of Industry 4.0

Meissner, Hermann; Ilsen, Rebecca; Aurich, Jan C.

doi:10.1016/j.procir.2016.06.113

Cited by 112 publications

(56 citation statements)

References 25 publications

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“…In this type of architecture, the individual elements must have the ability to process information and to make and execute decisions, thereby becoming autonomous. One interesting discussion and comparison of centralized, decentralized and hybrid control architecture in I4.0 was presented in [75]. They noted the strengths and weaknesses of each type of architecture leading to I4.0.…”

Section: Control Systemsmentioning

confidence: 99%

“…Such architecture is fitted to a dynamic environment and quickly adapts to change. Despite these benefits, decentralized control architecture is challenged by the effort required to coordinate single modules, each attempting to pursue its own objective and potentially disrupting the global objective of the system [75]. Hybrid architectures (a mixture of centralized and decentralized architectures) have good properties with respect to such issues and have manifold possibilities for applications.…”

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confidence: 99%

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Requirements of the Smart Factory System: A Survey and Perspective

et al. 2018

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Abstract:With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories.We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.

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Section: Control Systemsmentioning

confidence: 99%

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confidence: 99%

Requirements of the Smart Factory System: A Survey and Perspective

et al. 2018

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“…As for production companies of the Industry 4.0 it is logical to study two classes of ontologies [12,13]:…”

Section: Ontology In Industry 40 Companymentioning

confidence: 99%

The ontology in description of production processes in the Industry 4.0 item designing company

Gurjanov

Заколдаев²,

Шукалов³

et al. 2018

J. Phys.: Conf. Ser.

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Abstract. The task of production organization provision development to solve a practical task to describe of the item designing company manufacturing processes based on ontology is being studied. Ontology is a kind of manufacturing processes semantic description. Ontologies are necessary to link the technological equipment models and manufacturing processes which exist in the level of Internet of Things with their digital twins which exist in the level of Internet of services.

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“…However, coordinating a single machine that attempts to pursue its objective and at the same time pursuing the global objective of the system remains a challenge for the control system. As such, most of the control systems are a mixture of centralized and decentralized architectures (Brennan, 2000) that cannot fully address the requirement of Industry 4.0 (Meissner, Ilsen, & Aurich, 2017). Also, there is no concrete information on how it can enable the management, interoperability, and control of data inside a smart factory.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

Banadaki¹

2019

JCSIT

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Additive manufacturing (AM) is a crucial component of a smart factory that promises to change traditional supply chains. However, the parts built using state-of-the-art 3D printers have noticeable unpredictable mechanical properties. In this paper, a machine learning (ML) model is proposed as a promising approach to improve the underlying failure phenomena in the AM process. The paper also describe how a ML model can be distributed to form an interactive learning network of smart AM components to fulfil the Industry 4.0 requirements including self-organization, distributed control, communication, and real-time decision-making capability.Currently, almost all AM machines have only limited sensing capabilities that are mostly inaccessible to the users, or completely "open-loop" system operating without any feedback measurement systems for correction during the process. However, future AM machines must be a smart system that can perform self-monitoring, self-calibrating, and quality self-controlling in real-time. The gap between the smart factory and existing manufacturing systems can be bridged concerning the automation, flexibility, and reconfigurability of AM machines in an interactive distributed network as a natural way of scaling up learning algorithms.

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Analysis of Control Architectures in the Context of Industry 4.0

Cited by 112 publications

References 25 publications

Requirements of the Smart Factory System: A Survey and Perspective

Requirements of the Smart Factory System: A Survey and Perspective

The ontology in description of production processes in the Industry 4.0 item designing company

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

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