An Example of Modeling Manufacturing Systems Using Petri Nets and the IEC 61499 StandardVALENTIN VLAD, CALIN CIUFUDEAN, ADRIAN GRAUR, CONSTANTIN FILOTE Electrical Engineering and Computer Science Department University of Suceava Universitatii 13, RO-720229, Suceava ROMANIA {vladv | calin | adriang | filote}@ eed.usv.roAbstract: - Manufacturing Systems are becoming more and more complex and therefore it becomes a hard task to control them efficiently and in real time. Given a FMS model and a specification of the desired behaviour the goal of our work is to find an appropriate distributed controller that will perform in closed-loop with the engines according to the desired behaviour. In order to achieve this goal we model the FMS with Petri nets, for optimizing the systems behaviour and to eliminate the potential conflicts in sharing the system resources. Based on this Petri nets model, and according to the IEC 61499 standard specifications, we implement an RFID-based method to automatically determine the system configuration, along with a solution for optimum and distributed leading of product components. This paper presents some aspects related to the modelling process of the considered system, based on Petri nets and IEC 61499 Function Blocks. Key-Words: - Model-based control, Petri nets, IEC 61499, RFID

1 IntroductionManufacturing control systems are becoming more and more complex and highly automated, making it a hard task to achieve an efficient and real time control system. The main limitation of such systems is the lack of structure in controlled automata and the large number of states of the related state transition structures. The large number of states resides on the exhaustive searches of overall system behavior and result finally in state-space explosion problems. One way of dealing with these problems is to model the control system with Petri nets (PNs). PNs modeling is more compact than the automata approach and is better suited for modeling systems with parallel and concurrent activities. In addition, PNs has a friendly graphical representation with a powerful algebraic formulation and, thus, it has generated intense interest from scientists and researchers [5]. Another recently developed tool for modeling and simulating distributed control systems is the IEC 61499 standard. This standard defines a software paradigm built on the basis of FB (Function Block) that encapsulates data along with its behavior in a form that is similar to physical entities, i.e. electronic circuits or hardware elements. In designing and implementing state machine control, Petri nets are suitable for defining the desired sequence of operations for the controlled machine or process, as well as possible abnormal sequences which may occur. The IEC 61499 standard may be used for informal

modeling of the machine or process behavior, using simulation models, or to define the appropriate statemachine controllers, typically as the ECCs (Execution Control Charts) and algorithms of basic function block types. Another aspect addressed in this paper is the applying of the RFID technology in intelligent distributed manufacturing systems. RFID (Radio Frequency Identification) is a new automatic identification technology, which has many advantages, such as long-distance contact, programmable, bigger storage and more flexible memory. This technology is very suitable for data acquisition and process control in the industrial manufacturing sites, due to its characteristics: waterproof, antimagnetic, high temperature resistance, etc. Applying RFID technology to the manufacturing processes enables the possibility to obtain real-time information about the physical items involved in a certain process. This information may be used to improve the production efficiency and reduce the production cost. Also, the production data for a certain component, like operations, quality and time, can be written into RFID tags associated to the component allowing the system to be more decentralized and the process of production more flexible [3, 7]. An interesting RFID driven control scheme for production control is presented in [8], where the authors focused in applying the holonic concept to production

control in order to lessen the excess production and decrease the lead time. The paper is organized as follows: the next section introduces our demonstration environment and the considered manufacturing process. Section 3 presents the proposed PN based model, and Section 4 shows an under work modeling application based on the IEC 61499 standard. Conclusions are given in the last section.

The presented test bench simulates a simplified manufacturing system in which some aspects of a real manufacturing system are neglected, such as buffering the components at production facilities to keep free the main path or the process of taking away and placing back the components on the conveyor belt.

2.1. The considered processIn Fig. 2 a schema of the demonstration environment is presented. The simulated production facilities are called PF1, PF2, PF2, and are placed each on one of the three production flows. The RFID tag attached to each component transported on the conveyor belts contains all the operations to be executed for the component, chronologically ordered. Also, the RFID tag contains a field in form of an integer whose value represents the index of the next operation. Firstly, the value of this index is 0, pointing to the first operation from the list. Each time a production facility ends off an operation for a component, it must increment this index to point to the next operation that must be executed. When all operations are executed, the component is considered an end product and must be lead toward the conveyor output.

2 The demonstration environmentOur demonstration environment is built around a closed loop Bosch Rexroth conveyor with three flows (Fig. 1). This modular conveyor, compatible with the Basic Mechanic Elements provided by Rexroth, allows the system to be easily extended or reconfigured [2]. All sensors and actuators related to the movement of the product components on the conveyor belts are connected in a Profibus network and controlled by an IndraLogic L40 system. On each flow a microcontroller-based station is installed, which can read and modify the content of an RFID tag by means of an RFID tag reader/writer. The role of each station is to simulate a production facility that can execute one or more operations on a product component. Each component moving on the conveyor belts has an RFID tag attached, where all the operations to be executed by the production facilities to the component were previously written.SW1 SW2

Flow 3 Flow 2

PF3SW10 1

PF2

PF1Flow 1

SW20

1

IN

OUT

Fig. 2. Schematic of the system Let assume each station can execute a single operation to a component with respect to the next constraints: Machine PF1 can execute operation O1 only to a unprocessed component; Machine PF2 can execute operation O2 only to a unprocessed component; Machine PF3 can execute operation O3 only to a component previously processed by machine PF1 or PF2. As a result, an unprocessed component, loaded at the conveyor input may have written in its associated RFID tag one of the following operation sets:{O1 }, {O2 }, {O1 , O2 }, {O1 , O3 }

Fig. 1. The Bosch Rexroth Conveyor The route switches SW1 and SW2 are provided with controllers with RFID tag reader/writers which allow them to read the content of RFID tag accompanying the components transported on the conveyor belts. When a component is closing with a branch, the controller may read the next operation to be executed for the component and can determine which production facility is appropriate to execute it. Then it will instruct the conveyor controller to commute the switch to direct the component to the destination so as the time needed for an operation to be completed is the minimum.

.

In other words, the considered system is able to make four different end product types, corresponding to each operation set.

P3

3 Petri Net based modelIn this paper PNs are used to design the supervisory system when the process is fully automatized by using RFID tags to machine parts, respectively the automatic controllers autonomously controls the manufacturing system without external intervention. We assume that the reader is familiarized with Petri nets, or we address the specific given references [1, 10]. Fig. 3 illustrates the Petri net model for the considered process. The notations for the PNs are mentioned in Table 1.P4

End product is in the output buffer Unprocessed machine part is processed in station B Unprocessed machine part is processed in station A Machine part is processed in station C

T3 T4

SW2 = 0 SW2 = 1

P5

T5

SW1 = 1

P6

T6

SW1 = 0

4 IEC 61499 based model4.1. The IEC 61499 standardThe IEC 61499 standard defines an open architecture for the next generation of distributed control and automation. The key elements of distributed control architecture under IEC 61499 are application, device and resource. An application is a related set of functions that must talk to each other to fulfill a control task. A device is a control unit having one or more processors. It interfaces to the physical I/O and also communicates with other devices on the network. A resource is essentially a processor on which part of a distributed application will run. The programming unit of the IEC 61499 is the Function Block (FB). It is the basic building block from which entire applications may be built. There are three types of function blocks: basic function blocks, composite function blocks and service interface function blocks. A basic function block (Fig. 4a) executes an elemental control function, such as reading a sensor or setting the state of an actuator, and contains algorithms and an execution control chart (ECC). Basic function blocks may be combined together in a composite function block (Fig. 4b), to encapsulate a higher-level control function. The service interface function block provides the communication services among devices [9].

Fig. 3. The Petri Net model for the process introduced in section 2 Table 1. Notations for the PN's in Fig. 3Place P0 Description Unprocessed machine part is in the input buffer Unprocessed machine part or end product is between input and output points of the conveyor Unprocessed machine part is between conveyor output point and SW2 Trans. T0 Description Unprocessed machine part is laid on the conveyor Unprocessed machine part passes the conveyor output point without being ejected

P1

T1

P2

T2

End product is ejected from the conveyor

Event inputs

Event outputs

Event inputs

Event outputs

ECC Type identifier Algorithms

Execution Control Type identifier

Internal variables

Input variables

Output variables

Input variables

Output variables

a) Basic FB

b) Composite FB

Fig. 4. Basic and composite FB under IEC 61499 (from [4])

Using FBDK each FB Type (FBT) can be translated to a class in Java. Then each of the FBs, whether usermade or built-in, can be tested for the reliability of its designed functionalities. Moreover, applications, both stand-alone and distributed can be run along with a graphical interface for visualization and parameter variation. A useful visualization package, working with FBDK and used in this modeling application, is the vhmi package, available for download at www.fb61499.com. This package allows one to build complex systems from simple images which can be freely moved and rotated on a drawing form, overstepping in this way the limitations of visualization FBs included in the FBDK standard version.

4.3. The IEC61499 modeling application 4.2. Function Block Development Kit (FBDK)The modeling application is developed using the development kit for functions block FBDK licensed by Rockwell Automation Company [6]. FBDK represents an effort of the Holonic Manufacturing Systems consortium, and allows the users to define function blocks and to use built-in and/or user defined FBs in order to design elements of a distributed control application. This subsection presents an under development application for modeling and simulating of the system and process presented in section 2. In the IEC 61499 simulation model (Fig. 5) production facilities PF1 and PF2 (introduced in section 2) are represented by milling machines, labeled as M1 and M2. Production facility PF3 is represented by an assembly robot, labeled as Robot.

Fig. 5. The visualization and the HMI display Six component/product types are defined; each of them is represented by a specific picture, according to the Table 2. In accordance with the operational constraints introduced in section 2, the production facilities M1, M2, and Robot are able to do the following operations:

M1 can process a raw material component into a type A product ; M2 can process a raw material component into a type B product ; Robot can create type C and D composite products by combining a type A or B product

and a PEG component. The PEG components are assumed to be supplied to the production facility Robot by another system. Table 2. Defined component/product typesType Raw material component PEG component Type A product Type B product Type C product Type D product Yellow ring Green ring Yellow peg-ring combination Green peg-ring combination Picture Description Yellow disc

movement speed are defined as input variables of the Function Block CnvArc.

Fig. 7. The inside of Function Block CnvArc Once activated by receiving the LOAD event, a CnvArc or CnvLine FB will display the associated image at the start position and will move it, on each CLK event, on a trajectory and with a speed defined by its parameters. When the image reaches the end position, the FB will generate an output event for activating the next CnvArc or CnvLine, and simultaneously it disables itself. Each conveyor flow is modeled by a composite FB, encapsulating CnvArc and CnvLine FBs. Fig. 8 illustrates the internal structure of the Function Block Flow_1, designed to model the Flow 1 of the conveyor. It may be seen that this FB encapsulates two CnvArc and two CnvLine type FBs.

The conveyor paths are drawn on a background image. The machine parts and route switches (SW1 and SW2) are also represented by images, displayed, moved and rotated by means of two main FBs from the vhmi visualization package: Render and RenderRot. The modeled transportation system is consisting of three flows, like the Bosch Rexroth conveyor, presented in section 2. These flows are labeled as Flow 1, Flow 2, and Flow 3 in Fig. 5. Each flow can be decomposed in simple geometrical elements, like arcs of circle and lines. For example, as is illustrated in Fig. 6, Flow 1 is composed of two lines and two arcs of circle, each one measuring 180 degrees. Similarly, Flow 2 is composed of three arcs of circle, each one measuring 90 degrees, and two lines. As a result, the movement of a component on the conveyor paths may be assured by building function blocks able to move an image on a linear, respectively a circular trajectory. For this reason, two composite FB types were defined: CnvArc and CnvLine.

Fig. 8. The inside of Function Block Flow_1 The FBs modeling the conveyors flows, Flow_1, Flow_2, and Flow_3, are encapsulated in another FB, called Cnv_1P, which assures the transportation of one machine part on all of the conveyors paths ( Fig. 9). Finally, the composite Function Block Cnv_3P (Fig. 10), encapsulating three Cnv_1P type FBs, is designed for simulating a conveyor able to transport simultaneously three machine parts. The number of machine parts may be easily increased by adding new Cnv_1P type FBs to this FB.

180 circle arc

Lines

Fig. 6. Conveyors Flow 1 Fig. 7 depicts the inside of the Function Block CnvArc. The input events and variables of a Render FB type are controlled by Function Blocks CnvCtrl and CircleArc in order to assure the movement of the loaded image along an arc of circle. The maximum angle of the arc, the coordinates of the centre, the radius, and the

Fig. 9. The inside of Function Block Cnv_1P

is to detect the presence and the type of the machine parts, and to send this information to the associated production facilities. The model for an RFID sensor is composed of two parts: (a) a component for capturing and transmission the information about the machine parts, and (b) a component for visualization. The function of the visualization component is to signalize the presence of a machine part by turning ON or OFF a LED (simulated by means of two images). The component for machine part detection (Fig. 12) is encapsulated in the Function Blocks used for modeling the conveyors flows (Flow_1, Flow_2, and Flow_3). For simplicity, we have considered a single RFID sensor, respectively a single production facility on a flow.

a) The interface

b) The inside of FB

Fig. 12. The Function Block PosSens Fig. 10. The internal structure of Function Block Cnv_3P. The route switches For simulating the route switches SW1 and SW2, the Function Block RenderRot, from vhmi package is used. The rotational angle, as well the movement step, are controlled by a SW_Ctrl type FB (Fig. 11). The production facilities The production facilities are simulated by means of two composite FB types: Machine (Fig. 13) and Robot. These FBs receive information regarding the presence and the type of machine parts from the associated RFID sensors, and process them, according to the required operations, by changing their representation image.

Fig. 13. The inside of Function Block Machine

a) The interface

b) The inside of FB

5 ConclusionThe concepts and FBs developed in this work allow one to quickly build models for any type of transportation system based on conveyor belts and few robotized working stations, respectively being the FMS we worked with in the discrete event laboratory of our university. In order to build a distributed controller, that optimize the considered FMS we first model the system with Petri

Fig. 11. The Function Block Switch, designed for modeling the conveyors switches. The RFID sensors The RFID sensors, labeled as S1, S2, and S3 in Fig. 5, are placed in front of the production facilities. Their role

nets and then we applied the model to the IEC 61499 standard. We actually built several PNs models and we have calculated the range of cycle times for these models driven by different scenarios for controllers. There is no doubt that the RFID driven controllers ensures the optimum cyclic time for the PNS models and therefore it enhances the flexibility and availability of manufacturing systems.

References: [1] Bacelli, F. and Lin, Z. (1992). Compression properties of stochastic decision free Petri nets. IEEE Trans. Autom. Contr., 37(12), pp. 1905-1920. [2] Bosch Rexroth (2008). Bosch Rexroth web site: http://www.boschrexroth.com [3] Brusey, J., Fletcher, M., Harisson, M., Thorne, A., Hodges, S., and McFarlane, D. (2003). Auto-ID Based Control Demonstration. Phase 2: Pick and Place Packing with Holonic Control. Discussion Paper. Cambridge University, Cambridge, UK. [4] Christensen, J.H. (2003). The IEC 61499 Standard: Concepts and R&D Resources, in Tutorial at 1st

IEEE Intl. Conf. Industrial Informatics INDIN03, Canada. [5] Ciufudean, C., Larionescu, A., and Popescu, D. (2005). Diagnosability of Flexible Robotic Manipulators for Railway Systems. Advances in Electrical and Computer Engineering, 5(1), pp.3340. [6] FBDK (2008). Function Block Development Kit available at http://www.holobloc.com [7] Hua, J., Sun, H., Liang, T., and Lei, Y. (2008), The Design of Manufacturing Execution System Based on RFID. Workshop on Power Electronics and Intelligent Transportation System, pp. 8-11. [8] Kamioka, K., Kamioka, E., and Yamada, S. (2007). An RF-ID driven holonic control scheme for production control systems. Proceedings of the 2007 International Conference on Intelligent Pervasive Computing, pp. 509-514. [9] Vyatkin, V. (2007). IEC 61499 Function Blocks for Embed-ded and Distributed Control Systems Design. Instrumentation Society of America, USA. [10] Zurawski, R. and Zhon, M.C. (1994). Petri nets and industrial applications: a tutorial. IEEE Trans. Ind. Electr., 41(6), pp 567-583.