Software-based smart factory networking

2016 ◽  
Vol 64 (9) ◽  
Author(s):  
Robert Bauer ◽  
Roland Bless ◽  
Christian Haas ◽  
Markus Jung ◽  
Martina Zitterbart
Keyword(s):  
Communication Network ◽  
Software Defined Networking ◽  
Smart Factory ◽  
Network Functions ◽  
Smart Factories

AbstractThis paper describes a framework for software-based networking in smart factories (SF) that enables them to easily adapt the communication network to changing requirements. Similar to cloud-based systems, such SFs could be seen as production clusters that could be rented and configured as needed. The SF network utilizes software-defined networking (SDN) combined with network functions virtualization (NFV) in order to achieve the required flexibility. This paper presents and discusses

scholarly journals «Smart Factories» as a Promising Direction for Digitalization of Enterprises

2021 ◽  
Vol 10 (525) ◽  
pp. 363-367
Author(s):  
I. V. Yatskevych ◽  
◽  
N. D. Maslii ◽  
Keyword(s):  
Business Processes ◽  
Business Environment ◽  
Production Costs ◽  
Smart Factory ◽  
Automated Control ◽  
Promising Direction ◽  
Level Of Automation ◽  
Production Flexibility ◽  
High Level ◽  
Smart Factories

The work examines the role, problems and prospects of a «smart factory» in the conditions of digitalization of enterprises, taking into account imbalance of the business environment. It is defined that in modern conditions, most enterprises focus on automating business processes and increasing efficiency, and only a minority of them transforms the business model, which is grounded on an insufficient level of maturity for drastic changes. It is substantiated that the so-called «smart factory» is a promising direction for the development of digitalization of enterprises, taking into account the imbalance of the business environment. It is noted that the «smart factory» is an environment wherein machines and equipment can improve processes through automation and self-isolation. At the same time, they are aimed at mass production of articles, while maintaining a maximum production flexibility. These requirements are ensured due to the high level of automation and robotization of the enterprise. Automated control systems for technological and business processes are also widely used due to their consistency at each stage of interaction. The study of the content of the concept of «smart factory» allowed to determine and systematize its functions (planning, logistics, supply chain, product development), advantages (accessibility, optimization, proactivity, flexibility), segmentation, problem groups (personnel, technologies, business process). It is specified that the development and implementation of «smart factories», despite the advantages of the latter, can be difficult and risky. Enterprises that have succeeded with the introduction of «smart factories» can increase production and value by reducing production costs, improving quality and flexibility, as well as reducing the time for the market entrance.

scholarly journals Honeypot Coupled Machine Learning Model for Botnet Detection and Classification in IoT Smart Factory – An Investigation

2021 ◽  
Vol 335 ◽  
pp. 04003
Author(s):  
Seungjin Lee ◽  
Azween Abdullah ◽  
N.Z. Jhanjhi ◽  
S.H. Kok
Keyword(s):  
Machine Learning ◽  
Process Management ◽  
Denial Of Service ◽  
The United States ◽  
Smart Factory ◽  
High Detection Rate ◽  
Botnet Detection ◽  
Detection Model ◽  
Log File ◽  
Smart Factories

In the United States, the manufacturing ecosystem is rebuilt and developed through innovation with the promotion of AMP 2.0. For this reason, the industry has spurred the development of 5G, Artificial Intelligence (AI), and Machine Learning (ML) technologies which is being applied on the smart factories to integrate production process management, product service and distribution, collaboration, and customized production requirements. These smart factories need to effectively solve security problems with a high detection rate for a smooth operation. However, number of security related cases occurring in the smart factories has been increasing due to botnet Distributed Denial of Service (DDoS) attacks that threaten the network security operated on the Internet of Things (IoT) platform. Against botnet attacks, security network of the smart factory must improve its defensive capability. Among many security solutions, botnet detection using honeypot has been shown to be effective in early studies. In order to solve the problem of closely monitoring and acquiring botnet attack behaviour, honeypot is a method to detect botnet attackers by intentionally creating resources within the network. As a result, the traced content is recorded in a log file. In addition, these log files are classified quickly with high accuracy with a support of machine learning operation. Hence, productivity is increase, while stability of the smart factory is reinforced. In this study, a botnet detection model was proposed by combining honeypot with machine learning, specifically designed for smart factories. The investigation was carried out in a hardware configuration virtually mimicking a smart factory environment.

scholarly journals Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019 ◽  
Vol 8 (5) ◽  
pp. 143 ◽  
Author(s):  
Rabab Benotsmane ◽  
György Kovács ◽  
László Dudás
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Optimal Operation ◽  
Added Value ◽  
Smart Factory ◽  
Motion Path ◽  
Robot Arm ◽  
Quantitative Research Methods ◽  
Smart Factories

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

scholarly journals Barriers of embedding big data solutions in smart factories: insights from SAP consultants

2019 ◽  
Vol 119 (5) ◽  
pp. 1147-1164 ◽  
Author(s):  
Shuyang Li ◽  
Guo Chao Peng ◽  
Fei Xing
Keyword(s):  
Big Data ◽  
Project Managers ◽  
Smart Factory ◽  
Business Managers ◽  
Structured Interviews ◽  
Content Type ◽  
Systems Research ◽  
Information Systems Research ◽  
Analytical Tools ◽  
Smart Factories

Purpose Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers. Design/methodology/approach The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach. Findings A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers. Originality/value This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

SDN and NFV in the mobile core: Approaches and challenges

2015 ◽  
Vol 57 (5) ◽  
Author(s):  
Michael Jarschel ◽  
Arsany Basta ◽  
Wolfgang Kellerer ◽  
Marco Hoffmann
Keyword(s):  
Mobile Networks ◽  
Research Question ◽  
Software Defined Networking ◽  
Building Blocks ◽  
Core Network ◽  
Open Research ◽  
New Concepts ◽  
Network Functions ◽  
Development Paths ◽  
Mobile Core Network

AbstractThe introduction of Software Defined Networking (SDN) and Network Functions Virtualization (NFV) has transformed the way networks will be built in the future. This development also applies to mobile networks and their evolution. How the SDN and NFV concepts will be integrated exactly is still an open research question with multiple approaches and techniques in discussion. This article provides an overview of the current discussion points with regard to development paths, building blocks, deployment scenarios, and the opportunities and challenges of the new concepts in the mobile core network context.

scholarly journals An improved cyber-physical systems architecture for Industry 4.0 smart factories

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878419 ◽  
Author(s):  
Jehn-Ruey Jiang
Keyword(s):  
Vertical Integration ◽  
Physical System ◽  
Industry 4.0 ◽  
Smart Factory ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Systems Architecture ◽  
Physical Systems ◽  
Industrial Internet ◽  
Smart Factories

The cyber-physical system is the core concept of Industry 4.0 for building smart factories. We can rely on the ISA-95 architecture or the 5C architecture to build the cyber-physical system for smart factories. However, both architectures emphasize more on vertical integration and less on horizontal integration. This article proposes the 8C architecture by adding 3C facets into the 5C architecture. The 3C facets are coalition, customer, and content. The proposed 8C architecture is a helpful guideline to build the cyber-physical system for smart factories. We show an example of designing and developing, on the basis of the proposed 8C architecture, a smart factory cyber-physical system, including an Industrial Internet of Things gateway and a smart factory data center running in the cloud environment.

scholarly journals Layout design for flexible machining systems in FCIPS and convertibility to CPS module in smart factory

2017 ◽  
Vol 4 (17) ◽  
pp. 5-28 ◽  
Author(s):  
Yoshimi ITO
Keyword(s):  
Communication Network ◽  
Flexible Manufacturing ◽  
Smart Factory ◽  
Production Structure ◽  
Information Communication ◽  
Crucial Issue ◽  
Physical Systems ◽  
Flexible Manufacturing Cells ◽  
Near Future ◽  
Original Concept

Although being not in accordance with the original concept proposed in the “Industrie 4.0”, the smart factory has been gradually applied to the practice. In contrast, we can observe that nearly all discourses, suggestions and discussions have been carried out without considering the convertibility of flexible manufacturing in FCIPS (Flexible Computer-Integrated Production Structure), which is the utmost leading facility within the industrial nation, to the CPS (Cyber Physical Systems) module in the smart factory. Admitting the powerful potentiality of the smart factory, at crucial issue is to discuss to what extent and how the technological and human resources so far accumulated in FCIPS are available for the smart factory. This paper proposes, first, the conceptual drawing of the smart factory on the basis of the concept of FCIPS, and then suggests the similarity of both the concepts. In fact, the smart factory consists of cloud computing, information communication network and CPS modules, whereas FCIPS consists of CIM, information communication network and a group of FMCs (Flexible Manufacturing Cells). Then, the paper describes the present and near future perspectives of the CPS module and FMC, especially placing the stress on machining, and asserts the convertibility of FMC for “One-off Production with Keen Machining Cost” to the CPS module. Finally, the paper summarizes the research and engineering development subjects in FCIPS and the smart factory necessary to be investigated hereafter together with detailing one leading subject, i.e. methodology to incorporate the human-intelligence into CIM.

Semantische Kommunikationsschnittstellen zur Zustandsüberwachung im Karosseriebau/Standardised I4.0 communication interfaces – Requirements and measures to establish I4.0 principles in modern production systems

2016 ◽  
Vol 106 (10) ◽  
pp. 699-704
Author(s):  
H. Fleischmann ◽  
J. Kohl ◽  
A. Blank ◽  
M. Schacht ◽  
J. Fuchs ◽  
...  
Keyword(s):  
Communication Networks ◽  
Industry 4.0 ◽  
Production Systems ◽  
Industrie 4.0 ◽  
Efficient Implementation ◽  
Smart Factory ◽  
Vehicle Body ◽  
Body Construction ◽  
Semantic Communication ◽  
Smart Factories

Industrie 4.0-Technologie verspricht Unterstützung bei der Erfüllung komplexer Produktionsaufgaben. Bisher verhindern jedoch historisch gewachsene, industrielle Kommunikationsnetze durch die oft wenig semantische, strikte Kommunikation entlang der bestehenden Ebenen der Automatisierungspyramide eine effiziente Umsetzung der Prinzipien von „Smart Factories“. Diese Veröffentlichung thematisiert die Entwicklung semantischer Kommunikationsschnittstellen am Beispiel des Karosseriebaus der Audi AG.   Industry 4.0 technology promises to support the fulfillment of complex production tasks. Even today, historically grown industrial communication networks prevent an efficient implementation of smart factory principles, especially due to a lack of semantics and the strict communication along the existing layers of the automation pyramid. This publication focuses on the development of semantic communication interfaces using the example of the digitalization of the vehicle body construction at the Audi AG.

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