scholarly journals The Industry 4.0 technological and information processes cyber-modelling

2021 ◽  
Vol 2094 (4) ◽  
pp. 042062
Author(s):  
A V Gurjanov ◽  
D A Zakoldaev ◽  
I O Zharinov ◽  
O O Zharinov
Keyword(s):  
Production Systems ◽  
Cyber Physical Systems ◽  
Time Interval ◽  
Control Laws ◽  
Physical Systems ◽  
Production Processes ◽  
Information Models ◽  
Number Of Cycles ◽  
Cyber Physical Production Systems ◽  
Parameter Values

Abstract Cyber-modelling is the information models simulation process describing in a mathematical and formal logic languages (phenomenon models) how cyber-physical systems interaction mechanisms are united with different control laws and parameter values. The equation complexity represented in different levels of cyber-physical production systems hierarchy and non-equations of algebra, logic, end-subtraction, vector and matrices form in a discreet and uninterrupted times are defined with an aggregated number in the industrial automatics element control loop. The cyber-modelling is done for statistic and dynamic processes and equipment states being monitored in a virtual environment fixating actual in a time interval technological data. The cyber-modelling is done with integrated calculation equipment systems with parallel physical production processes of item manufacturing. The model time faster than physical processes let prognosticate the corrections modifying control signals and phase variables of cyber-physical systems united in an assembly conveyor. The cyber-modelling advantage is an expanded number of cycles to optimize the technological processes, which are calculated with integrated calculation systems using consecutive approximation method. They describe the cyber-modelling technology and propose the information models based on phenomenon cyber-physical production processes descriptions with general control theory terms, calculations and connection for hierarchy controlling structures.

scholarly journals V-model based development of cyber-physical systems and cyber-physical production systems

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 253-258
Author(s):  
Iris Gräßler ◽  
Dominik Wiechel ◽  
Daniel Roesmann ◽  
Henrik Thiele
Keyword(s):  
Production Systems ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Model Based ◽  
Cyber Physical Production Systems

scholarly journals Case Study of Building a Digital Twin for a Production System

2021 ◽  
Vol 29 (49) ◽  
pp. 51-58
Author(s):  
Fedor Burčiar ◽  
Pavel Važan ◽  
Simona Pulišová
Keyword(s):  
Production System ◽  
Industry 4.0 ◽  
Production Systems ◽  
Simulation Models ◽  
Cyber Physical Systems ◽  
Production Data ◽  
Physical Systems ◽  
Production Processes ◽  
Gathering Data

Abstract As the term of Industry 4.0 becomes more and more relevant with each passing day, it is up to researchers and companies to find solutions to integrating all the technologies it covers. One of those technologies, even though not highly developed, is simulation and building Cyber-Physical Systems for gathering data and improving the production processes. In the research described in this paper, we focused on integrating production data with simulation models in order to make the process of understanding and learning about complex production systems as simple and as quick as possible. This paper contains three sections. The first one introduces the theoretical fundamentals of our research. The second one focuses on the methods used to create a digital model of production system. The final one discusses the results of the conducted experiments, and their impact on further research.

Offene, intelligente Services für die Produktion*/Open, intelligent services for production using the example of integrated quality assurance

2019 ◽  
Vol 109 (03) ◽  
pp. 116-121
Author(s):  
J. Klöber-Koch ◽  
M. Schreiber ◽  
C. Linder ◽  
J. Bömelburg-Zacharias ◽  
G. Reinhart
Keyword(s):  
Quality Assurance ◽  
Data Analysis ◽  
Product Quality ◽  
Production Systems ◽  
Cyber Physical Systems ◽  
Improve Product Quality ◽  
Physical Systems ◽  
Useful Knowledge ◽  
Know How ◽  
Production Processes

In Produktionssystemen steigt die erzeugte Datenmenge durch die Integration von cyber-physischen Systemen und zusätzlichen Sensoren kontinuierlich an. Diese Daten enthalten potenziell nützliches Wissen zur Verbesserung der Produktqualität und der Produktionsprozesse. Unternehmen haben jedoch oftmals nicht das notwendige Know-how, die anfallenden Daten zu analysieren sowie das darin enthaltene Wissen zu extrahieren. Das Ziel des Forschungsprojekts „OpenServ4P“ ist es, Services zu entwickeln, die von Unternehmen ohne tiefgreifendes Know-how im Bereich Datenanalyse erfolgreich angewendet und an ihre spezifischen Anforderungen angepasst werden können. Im Folgenden wird ein Service für die automatisierte, integrierte Qualitätssicherung vorgestellt.   The amount of data generated in production systems increases continuously due to the integration of cyber-physical systems and additional sensors. These data contain potentially useful knowledge to improve product quality and production processes. However, companies often do not have the necessary know-how to analyze the data and extract the contained knowledge. The goal of the OpenServ4P research project is to develop services that can be successfully applied by companies without in-depth know-how in the field of data analysis and can be adapted to their specific requirements. In the following, a service for automated, integrated quality assurance is presented.

Step-based evolution support among networked production automation systems

2018 ◽  
Vol 66 (10) ◽  
pp. 849-858
Author(s):  
Christopher Haubeck ◽  
Heiko Bornholdt ◽  
Winfried Lamersdorf ◽  
Abhishek Chakraborty ◽  
Alexander Fay
Keyword(s):  
Production Systems ◽  
Cyber Physical Systems ◽  
Peer To Peer ◽  
Evolution Process ◽  
Physical Systems ◽  
Model Based ◽  
Automation Systems ◽  
Production Automation ◽  
Isolated Systems

Abstract Production systems are no longer rigid, unyielding, and isolated systems anymore. They are rather interconnected cyber-physical systems with an evolution process that needs to be supported. To enable reusability in evolution, a change-first cooperative support is proposed that relies on model-based evolution steps. The approach establishes a network-wide evolution process in a peer-to-peer networked community. Thus, moving towards decentralised marketplaces for evolution steps.

scholarly journals Nonfragile Integral-Based Event-Triggered Control of Uncertain Cyber-Physical Systems under Cyber‐Attacks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Shen Yan ◽  
Sing Kiong Nguang ◽  
Liruo Zhang
Keyword(s):  
Sufficient Conditions ◽  
Cyber Physical Systems ◽  
Cyber Attacks ◽  
Linear Matrix ◽  
Time Interval ◽  
System State ◽  
Network Resources ◽  
Physical Systems ◽  
Mean Square Stability ◽  
Event Triggered

This article studies the problem of nonfragile integral-based event-triggered control for uncertain cyber-physical systems under cyber-attacks. An integral-based event-triggered scheme is proposed to reduce the data transmissions and save the limited network resources. The triggering condition is related to the mean of system state over a finite time interval instead of instant system state. Random cyber-attacks in a communication channel are taken into account and described by a stochastic variable subject to Bernoulli distribution. A novel Lyapunov–Krasovskii functional based on Legendre polynomials is constructed, and the Bessel–Legendre inequality technique is employed to handle the integral term induced by the integral-based event-triggered scheme. Resorting to these treatments, sufficient conditions are established via a set of linear matrix inequalities to guarantee the asymptotic mean-square stability of the closed-loop system. Finally, a numerical example shows that the presented method is effective.

User-Guided Visual Analysis of Cyber-Physical Production Systems

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Tobias Post ◽  
Rebecca Ilsen ◽  
Bernd Hamann ◽  
Hans Hagen ◽  
Jan C. Aurich
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
Production System ◽  
High Performance ◽  
Visual Analysis ◽  
Production Systems ◽  
Physical Systems ◽  
Information And Communication ◽  
Cyber Physical Production Systems ◽  
Constituent Elements

Modern cyber-physical production systems (CPPS) connect different elements like machine tools and workpieces. The constituent elements are often equipped with high-performance sensors as well as information and communication technology, enabling them to interact with each other. This leads to an increasing amount and complexity of data that requires better analysis tools to support system refinement and revision performed by an expert. This paper presents a user-guided visual analysis approach that can answer relevant questions concerning the behavior of cyber-physical systems. The approach generates visualizations of aggregated views that capture an entire production system as well as specific characteristics of individual data features. To show the applicability of the presented methodologies, an exemplary production system is simulated and analyzed.

scholarly journals Energy Efficiency in Industry 4.0: The Case of Batch Production Processes

2020 ◽  
Vol 12 (16) ◽  
pp. 6631 ◽  
Author(s):  
Giancarlo Nota ◽  
Francesco David Nota ◽  
Domenico Peluso ◽  
Alonso Toro Lazo
Keyword(s):  
Energy Consumption ◽  
Industry 4.0 ◽  
Production Systems ◽  
Decision Makers ◽  
Batch Processes ◽  
Energy Losses ◽  
Manufacturing Processes ◽  
Batch Production ◽  
Production Processes ◽  
Cyber Physical Production Systems

We derived a promising approach to reducing the energy consumption necessary in manufacturing processes from the combination of management methodologies and Industry 4.0 technologies. Based on a literature review and experts’ opinions, this work contributes to the efficient use of energy in batch production processes combining the analysis of the overall equipment effectiveness with the study of variables managed by cyber-physical production systems. Starting from the analysis of loss cause identification, we propose a method that obtains quantitative data about energy losses during the execution of batch processes. The contributions of this research include the acquisition of precise information about energy losses and the improvement of value co-creation practices so that energy consumption can be reduced in manufacturing processes. Decision-makers can use the findings to start a virtuous process aiming at carbon footprint and energy costs reductions while ensuring production goals are met.

scholarly journals Simulation of Production Processes Involving Cyber-physical Systems

Procedia CIRP ◽  
2017 ◽  
Vol 62 ◽  
pp. 577-582 ◽  
Author(s):  
Jens F. Lachenmaier ◽  
Heiner Lasi ◽  
Hans-Georg Kemper
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Production Processes

scholarly journals Falsifying Cyber-Physical Systems – a Hybrid Optimization-Free and an Optimization-Based Line-Search Approach

2021 ◽  
Author(s):  
Zahra Ramezani ◽  
Koen Claessen ◽  
Nicholas Smallbone ◽  
martin fabian ◽  
Knut Åkesson
Keyword(s):  
Line Search ◽  
Random Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cyber Physical Systems ◽  
Benchmark Problems ◽  
Dimensional Parameter ◽  
Physical Systems ◽  
Search Approach ◽  
Parameter Values

<div>Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification, which can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods. In this paper, a method based on combining random parameters together with considering extreme combinations of parameter values is proposed. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification.</div>

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