Implementation of Load-Oriented Manufacturing Control

1995 ◽  
pp. 279-321
Author(s):  
Hans-Peter Wiendahl
Keyword(s):  
Manufacturing Control

THE INTELLIGENT PRODUCT IN MANUFACTURING CONTROL AND MANAGEMENT

2002 ◽  
Vol 35 (1) ◽  
pp. 49-54 ◽  
Author(s):  
Duncan McFarlane ◽  
Sanjay Sarma ◽  
Jin Lung Chirn ◽  
C Y Wong ◽  
Kevin Ashton
Keyword(s):  
Manufacturing Control ◽  
Intelligent Product ◽  
Control And Management

scholarly journals Multicast dataset synchronization and agent negotiation in distributed manufacturing control systems

2015 ◽  
Vol 48 (3) ◽  
pp. 1087-1092
Author(s):  
Octavian Morariu ◽  
Cristina Morariu ◽  
Theodor Borangiu ◽  
Silviu Raileanu
Keyword(s):  
Control Systems ◽  
Distributed Manufacturing ◽  
Manufacturing Control ◽  
Agent Negotiation ◽  
Distributed Manufacturing Control

scholarly journals Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation

2021 ◽  
Author(s):  
Dennis Bauer ◽  
Markus Böhm ◽  
Thomas Bauernhansl ◽  
Alexander Sauer
Keyword(s):  
Manufacturing Systems ◽  
Control Method ◽  
Great Influence ◽  
Learning System ◽  
Effective Control ◽  
Sufficient Information ◽  
Short Term ◽  
Data Format ◽  
Supply Networks ◽  
Manufacturing Control

AbstractIn manufacturing systems, a state of high resilience is always desirable. However, internal and external complexity has great influence on these systems. An approach is to increase manufacturing robustness and responsiveness—and thus resilience—by manufacturing control. In order to execute an effective control method, it is necessary to provide sufficient information of high value in terms of data format, quality and time of availability. Nowadays, raw data is available in large quantities. An obstacle to manufacturing control is the short-term handling of events induced by customers and suppliers. These events cause different kinds of turbulence in manufacturing systems. If such turbulences could be evaluated in advance, based on data processing, they could serve as aggregated input data for a control system. This paper presents an approach how to combine turbulence evaluation and the derivation of measures into a learning system for turbulence mitigation. Integrated in manufacturing control, turbulence mitigation increases manufacturing resilience and strengthens the supply network’s resilience.

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