scholarly journals Dynamic production control for flexibility in Cyber-Physical Production Systems using an autonomous transport system

Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 1160-1165
Author(s):  
Michaela Krä ◽  
Sebastian Hörbrand ◽  
Johannes Schilp
Keyword(s):  
Transport System ◽  
Production Control ◽  
Production Systems ◽  
Cyber Physical Production Systems ◽  
Dynamic Production

scholarly journals Simulation von Cyber-Physischen Produktionssystemen in einer Forschungsinfrastruktur

2020 ◽  
Author(s):  
Iris Gräßler
Keyword(s):  
Communication Technology ◽  
Information Acquisition ◽  
Industry 4.0 ◽  
Production Control ◽  
Production Systems ◽  
General Concept ◽  
Radio Network ◽  
Systems Research ◽  
Digital Twins ◽  
Cyber Physical Production Systems

The article describes the setup of an experimentation and validation environment by extending a production laboratory: All relevant elements of the production laboratory were equipped with computer systems, so-called "industry 4.0 boxes", and interconnected via a peer-to-peer radio network. The "industry 4.0 boxes" are used to upgrade dedicated sensors for recording machine behaviour and communication technology to be integrated into decentralized production control. In addition, digital twins were implemented to map machine and user behaviour, enable control and support information acquisition and processing. Thereby, a research infrastructure is created for research on potentials of cyber-physical production systems. Research outcomes will be used as a decision basis for companies and for validation of production optimizations. This paper describes the concept and implementation of industry 4.0 functionalities and derives a general concept of simulation platforms for CPPS.

scholarly journals Approach for an event-driven production control for cyber-physical production systems

Procedia CIRP ◽  
2019 ◽  
Vol 79 ◽  
pp. 349-354 ◽  
Author(s):  
Christoph Berger ◽  
Alexander Zipfel ◽  
Stefan Braunreuther ◽  
Gunther Reinhart
Keyword(s):  
Production Control ◽  
Production Systems ◽  
Event Driven ◽  
Cyber Physical Production Systems

scholarly journals Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems

2020 ◽  
Vol 4 (4) ◽  
pp. 108
Author(s):  
Bastian Engelmann ◽  
Simon Schmitt ◽  
Eddi Miller ◽  
Volker Bräutigam ◽  
Jan Schmitt
Keyword(s):  
Machine Learning ◽  
Production Control ◽  
Production Systems ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Medium Size ◽  
Data Set ◽  
Set Up ◽  
Cyber Physical Production Systems

The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.

Intelligente Werkstattfertigung*/Intelligent job shop manufacturing - Control mechanisms for job shop manufacturing of series products using the German “Industrie 4.0” approach

2015 ◽  
Vol 105 (04) ◽  
pp. 184-189
Author(s):  
E. Uhlmann ◽  
B. Schallock ◽  
F. Otto
Keyword(s):  
Production Control ◽  
Job Shop ◽  
Production Systems ◽  
Industrie 4.0 ◽  
Innovative Technology ◽  
Shop Floor ◽  
Control Mechanisms ◽  
Utilisation Rate ◽  
Cyber Physical Production Systems ◽  
And Control

Die „intelligente selbstorganisierende Werkstattproduktion“ (iWePro) folgt dem Konzept einer dezentralisierten Produktionssteuerung. Erstmalig wird die Anwendung der Selbstorganisation auf die Serienproduktion von Automobilkomponenten untersucht, die momentan nach Lean-Prinzipien für große Stückzahlen verkettet aufgebaut ist. Zukünftig soll mit dem Werkstattprinzip schwankenden Auslastungen entgegengewirkt werden. Die Fertigungssteuerung für die dadurch wahlfrei zugreifbaren Produktionsmaschinen lässt sich konventionell kaum, wohl aber mit Zukunftskonzepten und Industrie 4.0-Technologien umsetzen.   “Intelligent self-organizing shop floor production” (iWePro) uses the concept of decentralized production control solutions. For the first time, a concept of self-organization is applied to the production of car components, which are currently a moving line according to traditional lean production large batch principles. In the future, the traditional shop floor structure of disconnected machines should guarantee a higher utilisation rate but needs innovative technology and control mechanisms for cyber-physical production systems (CPPS).

scholarly journals Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 3-8
Author(s):  
Marvin Carl May ◽  
Lars Kiefer ◽  
Andreas Kuhnle ◽  
Nicole Stricker ◽  
Gisela Lanza
Keyword(s):  
Economic Model ◽  
Production Control ◽  
Production Systems ◽  
Multi Agent ◽  
Matrix Production

scholarly journals Development of a Decision Support System for 3D Printing Processes based on Cyber Physical Production Systems

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 348-353
Author(s):  
Rishi Kumar ◽  
Christopher Rogall ◽  
Sebastian Thiede ◽  
Christoph Herrmann ◽  
Kuldip Singh Sangwan
Keyword(s):  
Decision Support ◽  
3D Printing ◽  
Decision Support System ◽  
Support System ◽  
Production Systems ◽  
Cyber Physical Production Systems ◽  
Printing Processes
Sign in / Sign up

Export Citation Format

Share Document