scholarly journals Modeling and Simulation of Processes in a Factory of the Future

2020 ◽  
Vol 10 (13) ◽  
pp. 4503 ◽  
Author(s):  
Patrik Grznár ◽  
Milan Gregor ◽  
Martin Krajčovič ◽  
Štefan Mozol ◽  
Marek Schickerle ◽  
...  
Keyword(s):  
Intelligent Manufacturing ◽  
Modelling And Simulation ◽  
Planning And Control ◽  
Current Trends ◽  
The Future ◽  
Factory Of The Future ◽  
The Individual ◽  
And Control ◽  
Behavioural Management ◽  
Factories Of The Future

Current trends in manufacturing, which are based on customisation and gradually customised production, are becoming the main initiator for the development of new manufacturing approaches. New manufacturing approaches are counted as the application of new behavioural management patterns that calculate the retained competencies of decision-making by the individual members of the system agent; the production becomes decentralised. The interaction of the members of such a system creates emergent behaviour, where the result cannot be accurately determined by ordinary methods and simulation must be applied. Modelling and simulation will, therefore, be an integral part of the planning and control of the processes of factories of the future. The purpose of the article is to describe the use of modelling and simulation processes in factories of the future. The first part of the article describes new manufacturing concepts that will be used in factories of the future, with a description of modelling and simulation routing in the frame of Industry 4.0. The next section describes how simulation is used for the control of manufacturing processes in factories of the future. The included subsection describes the implementation of this suggested pattern in the laboratory of ZIMS (Zilina Intelligent Manufacturing System), with an example of a metamodeling application and the results obtained.

Eine bibliometrische Analyse/PPC and machine learning A bibliometric analysis

2020 ◽  
Vol 110 (04) ◽  
pp. 220-225
Author(s):  
Matthias Schmidt ◽  
Janine Tatjana Maier ◽  
Mark Grothkopp
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Dynamic Environment ◽  
Production Planning And Control ◽  
Manufacturing Companies ◽  
Comprehensive Review ◽  
Planning And Control ◽  
Individual Task ◽  
The Individual ◽  
And Control

Produzierende Unternehmen stehen in einem dynamischen Umfeld vor der Herausforderung eine zunehmende Datenmenge effizienter zu verarbeiten. In diesem Zusammenhang werden häufig Ansätze des maschinellen Lernens (ML) diskutiert. Der Beitrag stellt eine umfassende Aufarbeitung des Stands der Forschung bezogen auf den Einsatz von ML-Ansätzen in der Produktionsplanung und -steuerung (PPS) bereit. Daraus lässt sich der Forschungsbedarf in den einzelnen Aufgabengebieten der PPS ableiten.   In a dynamic environment, manufacturing companies face the challenge of processing an increasing amount of data more efficiently. In this context, approaches of machine learning (ML) are often discussed. This paper provides a comprehensive review of the state of the art regarding the use of ML approaches in production planning and control (PPC). Based on this, the need for research in the individual task areas of PPC can be derived.

Planning and Control Processes Across the Life Span: An Overview

1993 ◽  
Vol 16 (2) ◽  
pp. 131-143 ◽  
Author(s):  
Margie E. Lachman ◽  
Orah R. Burack
Keyword(s):  
Life Span ◽  
Well Being ◽  
Special Issue ◽  
Control Processes ◽  
Planning And Control ◽  
Integrate Control ◽  
The Individual ◽  
And Control ◽  
Future Work

We present a brief overview of the areas of planning and control to provide a context for the individual papers in this special issue. For both topics we consider development across the life span, subgroup variations (e.g. by gender), and correlates (e.g. well-being). We then explore potential linkages between planning and control. Our attempt to integrate control and planning is meant to stimulate future work which considers these processes together from a life span perspective.

scholarly journals AWARENESS AS AN IMPORTANT COMPONENT OF THE ARCHITECTURE OF THE FACTORIES OF THE FUTURE

2021 ◽  
Vol 1 (69) ◽  
pp. 6-9
Author(s):  
A. Dmitrieva
Keyword(s):  
Positive Impact ◽  
High Tech ◽  
The Future ◽  
Industrial Architecture ◽  
New Type ◽  
Manufacturing Facilities ◽  
Factory Of The Future ◽  
Factories Of The Future ◽  
Production Facilities

The article defines the Factory of the Future as a modern organizational concept of production and as a new type of industrial architecture. The concept of awareness as an important property of the architectural environment of the newest production facilities is disclosed. The main methods of its formation are listed and described. Examples of manufacturing facilities implementing awareness in its architecture are given. The conclusions about the positive impact of the awareness on the functioning of high-tech production facilities and Factories of the Future are made.

scholarly journals A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

2020 ◽  
Vol 10 (13) ◽  
pp. 4482 ◽  
Author(s):  
Adrien Bécue ◽  
Eva Maia ◽  
Linda Feeken ◽  
Philipp Borchers ◽  
Isabel Praça
Keyword(s):  
Cognitive Modeling ◽  
Monitoring And Control ◽  
Security Testing ◽  
Digital Twin ◽  
Digital Twins ◽  
Support Tools ◽  
The Future ◽  
And Control ◽  
Comprehensive Modeling ◽  
Factories Of The Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.

scholarly journals Energy Modeling and Power Measurement for Three-Wheeled Omnidirectional Mobile Robots for Path Planning

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 843 ◽  
Author(s):  
Linfei Hou ◽  
Liang Zhang ◽  
Jongwon Kim
Keyword(s):  
Path Planning ◽  
Power Consumption ◽  
Mobile Robots ◽  
High Mobility ◽  
Intelligent Manufacturing ◽  
Power Measurement ◽  
Research Focus ◽  
Planning And Control ◽  
Perfect Symmetry ◽  
And Control

Due to their high mobility, mobile robots (MR) are widely used in intelligent manufacturing. Due to the perfect symmetry of the MR of the three-wheeled moving chassis, it can move quickly in a crowded and complex factory environment. Because it is powered by a lithium battery, in order to improve its energy efficiency, we need to ensure that its power consumption is reduced as much as possible in order to avoid frequent battery replacement. The power consumption of MRs has also become an important research focus for researchers. Therefore, a power consumption modeling of the omnidirectional mobility of the three-wheeled omnidirectional mobile robot (TOMR) is proposed in this paper. When TOMR advances heading at different angles, the speed of each wheel changes dramatically. So, the power consumption of robots will also be greatly changed. In this paper, the energy and power consumption of the robot heading in different directions is analyzed and modeled by formulas. This research can be valuable for path planning and control design.

Virtual Reality for Manufacturing Engineering in the Factories of the Future

2015 ◽  
Vol 789-790 ◽  
pp. 1275-1282 ◽  
Author(s):  
Mohammed Bougaa ◽  
Stefan Bornhofen ◽  
Hubert Kadima ◽  
Alain Rivière
Keyword(s):  
Virtual Reality ◽  
Systems Engineering ◽  
Production Efficiency ◽  
New Technologies ◽  
Key Technology ◽  
Engineering Approach ◽  
The Future ◽  
Manufacturing Engineering ◽  
Factory Of The Future ◽  
Factories Of The Future

This paper discusses the possibilities of applying Virtual Reality (VR) technologies to Manufacturing Engineering, and in particular assesses its role in the Factory of the Future (FoF). We review, classify and compare the recommendations given by four major European reports on the challenges that have to be met for a successful deployment of the FoF, and we identify the potential contributions of VR to this vision in terms of new technologies, worker-factory relationship, modular infrastructure and production efficiency. We argue that VR can be a key technology to support the FoF at all levels of the Systems Engineering approach, either directly by applying it in standard engineering processes, or indirectly by leveraging other useful technologies.

scholarly journals A Framework for Multi-Agent UAV Exploration and Target-Finding in GPS-Denied and Partially Observable Environments

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4739
Author(s):  
Ory Walker ◽  
Fernando Vanegas ◽  
Felipe Gonzalez
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Search Problem ◽  
Planning Problem ◽  
Planning And Control ◽  
Points Of Interest ◽  
Multi Agent ◽  
The Individual ◽  
Partially Observable ◽  
And Control

The problem of multi-agent remote sensing for the purposes of finding survivors or surveying points of interest in GPS-denied and partially observable environments remains a challenge. This paper presents a framework for multi-agent target-finding using a combination of online POMDP based planning and Deep Reinforcement Learning based control. The framework is implemented considering planning and control as two separate problems. The planning problem is defined as a decentralised multi-agent graph search problem and is solved using a modern online POMDP solver. The control problem is defined as a local continuous-environment exploration problem and is solved using modern Deep Reinforcement Learning techniques. The proposed framework combines the solution to both of these problems and testing shows that it enables multiple agents to find a target within large, simulated test environments in the presence of unknown obstacles and obstructions. The proposed approach could also be extended or adapted to a number of time sensitive remote-sensing problems, from searching for multiple survivors during a disaster to surveying points of interest in a hazardous environment by adjusting the individual model definitions.

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