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Deploy the Phase I architecture in a form usable by SMEs who currently have no access to manufacturing system simulations or other advanced analytics. Expand and mature the Phase I implementation to additional questions, use cases, and classes of manufacturing systems. The result should be a prototype of a commercially viable software solution.

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Presentation on theme: "IV-2 Manufacturing Systems modeling"— Presentation transcript:

Topic Number:. Release Date: January 10, Open Date: January 10, Application Due Date: March 30, The events stimulate the dynamics causing a state transition system Bergero, F. Today with the changing market demands and economic globalization, there are expectations for highly flexible production systems, able to take advantage of new information technologies and changes in demand and event-based modeling offers the possibility of representing the dynamic behavior of processes.

The acquisition of the process model is an important element in the control systems realignment. For this purpose, several formalisms were developed that allow the modeling of discrete events variables. Among the formalisms commonly used in modeling, analysis and control of discrete event systems Van der Schaft A. This paper is structured as follows: the first section presents an introduction to the Discrete Event Systems; the next section presents both the DEVS atomic formalism and the coupled DEVS structural descriptions.

The third section describes an industrial process modeling containing discrete events variables, with the three workstations attached. For a long time, mathematical tools have been used to represent the behavior of systems governed by time or physical phenomena as differential equations.

Currently, and taking into account the technological developments made by man, such as computers, transportation systems, manufacturing, communications and others. Moreover, considering that their behavior is governed by events occurring asynchronously in time Van der Schaft A. This system is typically called discrete event DES , and its analysis is very complex, due to a series of requirements such as, copyrights issues, productivity requirements, limitations in response time and so on Hong, K.

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Branicky M. Modeling can be done through the system decomposition into smaller models, specifying the coupling between them. The smaller models are defined as the atomic model, the fundamental element, representing the processing "molecular" unity, and the second is the coupled model Alur, R. Where: X : Is the input events set of values. Y : Is the output events set of values. Meaning, that system's new state depends on one of the two transition functions, but not on both simultaneously. The model responds to external input events, according to its external transition function, in the absence of events in a specific time set by the time advance function, the model changes state according to the internal transition function and generates an output event for the other models.

The atomic models can interact through a coupled model. This allows for to the division of a modeling issue from a complex system into small units that can be coupled and make their representation easier Chen, C. Figure 3 shows a coupled DEVS diagram. With its several closed loops, the coupled DEVS modules can, not only be connected in cascading form, but also allows feedback configurations between the atomic models, thus permitting an industrial production system representation.


The coupled DEVS is formed of several atomic models D , connected through internal link and is defined by the tuple of equation 4 and present in the Figure 2. Verifiable according to the connections. Where: N, ip N : Represents the coupled model ip N input port. N, ip N , d, ip d : Represents the connection between the two ports. EOC : The set of output links that connects the outputs from one or more components to the coupled model input port.

Where: d, op d : Represents the component's op d output port. N, op N : Represents the composite model's op N output port. IC : It is the internal links set connecting the components' output ports to the coupled models input ports. Where: a, op a : represents the coupled model's ip N port of entry. Select: sets event priorities, even if an internal event is already scheduled for the same time. This section presents a study case to illustrate the DEVS formalism application in an industrial production process.

The experiment comprises a production line consisting of three workstations, coupled sequentially. See Figure 3.

Introduction to Simulation: System Modeling and Simulation

The production line model was based on each workstation's atomic model and their integration produced one coupled model. The first station is pneumatically operated, charged with supplying the raw material into a production line assembling process. The model shows this station as a black box, responsible for producing a consistent product on a support platform by completing the final product. In this manner, it can be displayed as a system with an input buffer, which serves the products to be processed by a server, which represents the delivery of the raw material to the production line.

The raw material from the previous station arrives at the next station that is in charge of the Lego type product assembling on a support platform. With the product assembling complete, it passes to a third workstation responsible for the product labeling, similar to the previous workstations, this workstation is connected to a server able to perform the designated activity with a product-receiving buffer. In the modeled system, each station consists of its input buffer associated with the server Px, configured in series to represent the sequential process.

IE REV: Advanced Manufacturing Systems Modeling and Analysis & Warehousing

The raw material to serve as support for assembling is represented by the elements ax, with an arrival rate linearly independent. The red circles imply that a server is handling an event busy and that a product in the queue is waiting for the service. Figure 3. Semeraro and B. Wiley, New York. Foster and H.

Manufacturing Systems Modeling and Analysis

Gaver and G. Gelenbe and G. MR Gittins and P. Academy of Sciences. Gordon and G. Gross and C. Graves and J.

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Logist Quart. Gray , A. Seidmann and K. Gunn , The mechanization of design and manufacturing , Scientific American, Sept. Halachmi and W. Hatvany , World Survey on C. Ho and X. Hutchinson and B. Hsu and C. FMSs, , 1, pp.