On Model-based Approach for Developing Control Systems of Event-Driven Manufacturing Systems

2006 ◽  
Author(s):  
Kayoko Takatsuka ◽  
Shigeyuki Tomita
Keyword(s):  
Control Systems ◽  
Manufacturing Systems ◽  
Model Based ◽  
Event Driven

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

Quantitative Models for Complex Physical Systems

2014 ◽  
Vol 1061-1062 ◽  
pp. 1144-1147
Author(s):  
Jun Fu ◽  
Jin Zhao Wu ◽  
Ning Zhou ◽  
Hong Yan Tan
Keyword(s):  
Quantitative Analysis ◽  
State Space ◽  
Control Systems ◽  
Chemical Reaction ◽  
Metric Space ◽  
Manufacturing Systems ◽  
Quantitative Model ◽  
Hybrid Automata ◽  
Physical Systems ◽  
The Difference

We present a quantitative model, called metric hybrid automata, for quantifying the behaviors of complex physical systems, such as chemical reaction control systems, manufacturing systems etc. Due to the introduction of a metric, the state space of hybrid automata forms a metric space, in which the difference of states can be quantified. Furthermore, in order to reveal the distance of system behaviors, we construct the simulation distance and the bisimulation distance, which quantify the similarity of system behaviors. Our model provides the basis for quantitative analysis for those complex physical systems.

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