Modeling Agile Manufacturing Cell using Object-Oriented Timed Petri net

Transcription

1 Modeling Agile Manufacturing Cell using Object-Oriented Timed Petri net Peigen Li, Ke Shi, Jie Zhang Intelligent Manufacturing Lab School of Mechanical Science and Engineering Huazhong University of Science and Technology ABSTRACT: An Object-Oriented Timed Petri-net (OOTPN) is proposed for modeling agile manufacturing cell (AMC). It consists of four phases: (1) grouping manufacturing resources into classes; (2) constructing general OOTPN block for each manufacturing resource class; (3) defining joint transitions and suitable control decision rules; (4) generating complete OOTPN model for agile manufacturing cell. This OOTPN approach possesses the characteristics of both object-oriented method and Petri-nets. It can describe the attributes and behaviors of AMC. Furthermore, the model based on this approach has the high degree of modularity, flexibility and reusability. In comparison with other modeling methodologies, the main advantage of OOTPN is that the cell model can be regenerated quickly by selecting or modifying appropriate pre-defined modeling objects when the agile manufacturing cell is reconfigured. INTRODUCTION Manufacturing enterprises have recognized that the rapid introduction of new and customized products to meet market demands is a key factor that ensures their survival from fierce competition, which in turn requires manufacturing systems be reconfigured quickly according to different products. The manufacturing cell is the basic unit for the manufacturing system, which carries out the final accomplishment of the production plan. The main task of a manufacturing cell is to accomplish assigned operations on assigned parts with reasonable cost in a time horizon and guarantee processing quality. In the future, it should be able to reconfigure rapidly to meet changing and unpredictable market demands. The concept of AMC (Agile Manufacturing Cell) arises from the idea of agile manufacturing. AMC could be a virtual manufacturing cell, that is to say, the physical layout of the manufacturing resources of a workshop is fixed, but the logic composition can be redesigned or reorganized to meet

2 different production requirements. The essential characteristic of AMC is that it can be reconfigured rapidly according to production tasks or state variation of a manufacturing environment. Its agile configuration brings additional values such as rapidly new product introduction, accommodation to unpredictable demand, higher equipment efficiency, etc. In order to accommodate the changes of production tasks and manufacturing environments, it is necessary to have suitable methodology and tools for rapid and agile configuration of manufacturing cells. To achieve this goal, rapid and reusable modeling techniques are demanded. There have been some important modeling methodologies such as queuing networks, mathematical programming, activity cycle diagrams, IDEF0 diagrams, Petri-nets, O-O (object-oriented), and so on. However, all of these methods have certain limitations. The assumption of some random processes and their parameters may not well reflect the real manufacturing processes. Activity cycle diagram cannot represent the relationships of conditions and events in discrete event dynamic systems. Although Petri-nets have been widely applied to modeling, controlling, and analyzing the system s dynamic behaviors due to the characteristics of the graphical representation and mathematical analysis of control logic, the efforts of modeling and analysis become quite cumbersome when the system becomes more complex. 5 Furthermore, these methods lack of reusability. The O-O modeling technique provides a one-to-one correspondence between the entities in a real system and the objects in its model that represent them. It possesses high modularity, maintainability and reusability. These advantages have led many researchers to apply O-O methods to manufacturing system modeling. 1-4,7 Nevertheless, a powerful tool supporting quantitative analysis and validation is still unavailable in the O-O method and its depiction to real system lacks abstraction and accuracy. This paper aims at developing an OOTPN (Object-Oriented Timed Pert-Net) modeling method for AMC modeling. This method is an instance of composite modeling, which possesses the characteristics of O-O technique and Petri-net. The rest of this paper is organized as follows. In Section 2, the characteristics of AMC and its requirements on system modeling are discussed. In Section 3, the definition of OOTPN is presented. Section 4 describes the modeling process of AMC using OOTPN method. An example is provided in Section 5. Finally, conclusions are given. PROBLEM STATEMENT Although there is yet no definitive science base for AMC, there are some desirable characteristics of AMCs. They are reusable, reconfigurable, and scalable. 6 Reconfigurability is an essential feature of AMC, which makes it accommodate to different production tasks or manufacturing environments. It ensures rapid introduction of new and customized products. Reusability and scalability ensure reconfiguration to be implemented at reasonable cost. Because of these characteristics, there are some problems need to be solved: 1) Cell reconfiguration. The primary goal of cell configuration is to form or reconfigure a manufacturing cell in terms of new products or changes of production tasks. Formation of AMCs relates to production task allocation, process planning and manufacturing resources allocation in the workshop environment. 2) Cell control and scheduling. Due to the dynamics of cells in the sense of agile manufacturing, AMCs should be apt to control and schedule. An effective schedule should be generated rapidly according to production tasks and current cell status. The cell scheduling and configuration depend on each other.

3 The OOTPN method proposed in this paper focuses on solving cell control and scheduling problems. The OOTPN model for a cell can be rapidly established by selecting (perhaps modifying) pre-defined modeling objects (i.e. manufacturing resources models, joint transitions and control decision rules). Joint transitions and control decision rules determine the relationships among manufacturing resources and depict the flow of material and information. After a scheme of cell configuration and scheduling is completed, performance analysis is needed to determine whether the control is appropriate to the tasks and manufacturing environments. If not, joint transitions and their corresponding control decision rules will be modified. The process may be performed repeatedly until satisfactory results are obtained. The OOTPN model can be used to explicitly analyze logic and dynamic behaviors of an AMC and finally generate an effective schedule. The results of model analysis are also an important feedback to the cell reconfiguration process. The goal of modeling AMCs is to maintain high system utilization and throughput under the constraints of due date and equipment status. OOTPN, a new approach for AMC should have following characteristics. 1) Models must be easily defined and implemented; 2) The manufacturing resources can be represented by the objects in an OOTPN, and the mutual relationships of resources can be directly mapped into joint transitions and control decision rules; 3) A model is configured by selecting or modifying appropriate modeling objects; 4) A model captures the relationships and interactions of modeling objects so as to describe control logic; 5) Model can be easily updated and reconfiguration should be convenient. OBJECT-ORIENTED TIMED PETRI-NET (OOTPN) From an object-oriented perspective, an AMC is composed of a number of physical objects (i.e. manufacturing equipment). Each physical object is a kind of manufacturing resource and has its own behaviors characterized by certain processes (e.g. milling). An OOTPN model here is able to describe mutually communicating physical objects and their interconnecting relations. Mathematically, a system can be defined as: S = (P T g K) in which P = a set of Timed Petri-net blocks = {P i, i = 1, 2,,I} P i = the Timed Petri-net block of physical object i T g = a set of joint transitions among Timed Petri-net blocks = {T gij, i, j = 1, 2,,I, i j} K = a set of control decision rules that decides how to fire a joint transition and which joint transition is fired. Objects in an AMC model are classified into physical, information, and control/decision object classes. Each Timed Petri-net model, P i ( i = 1, 2,, I), in an OOTPN model represents the activities performed by each physical object (e.g. manufacturing facility). The message-passing among models is achieved through joint transition T g. The firing condition of a joint transition is determined by the attached attributes and relative control decision rules. Whenever the firing decision of a joint transition needs to be determined, its corresponding control/decision object will select the most

4 suitable decision rule from the decision knowledge base according to the current system status (e.g. equipment status, bill-of materials). As a matter of fact, transition and state variation actually depict the production activities of the physical equipments. There exist material flow and information flow in a manufacturing system. The design and analysis of information flow are increasingly important for the manufacturing system. Places in OOTPN model are classified into resource and information places. Resource places represent states or conditions of manufacturing resources. The tokens in resource places represent status of resources in manufacturing system. Its flow represents material flow or status transition of the manufacturing system. The tokens in information places represent messages. The token transition means information flow. There exist two special kinds of places in OOTPN model, input places and output places. Input/output places are interfaces through which the TPN models of physical objects interact with each other. Input places of a TPN model for a certain machine receive tokens from other TPN models for other machines, output places of a TPN model send tokens to other TPN models. The input/output places in TPN models use encapsulation of O-O modeling for reference and enhance the model modularity. Timed Petri-net is adopted because it can describe the time of accomplishing a transition, which is advantageous to performance analysis of manufacturing systems. Therefore, P i can be defined as: P i = (SP,ST,F,W in which SP = a set of places = {SP r, SP m } SP r = a set of resource places = {sp r1,sp r2,,sp rn,sp inr1, sp inrj,sp outr1, sp outrk }, sp inr is input place, sp outr is output place SP m = a set of information places = {sp m1,sp m2,,sp mx,sp inm1, sp inmy,sp outm1, sp outmz }, sp inm is input place sp outm is output place ST = a set of transitions = {st 1,st 2, st s } SP ST SP ST= F (SPhST) (SThSP) = a set of directed arcs W = a set of weighting factors of the arcs. In the OOTPN model, the P i, which defines input/output places as mentioned above, can also be called OOTPN block i. MODELING PROCESS OF AMC USING OOTPN METHOD The OOTPN paradigm for AMC modeling consists of four phases: 1) To group manufacturing resources (i.e. equipment) into classes based on O-O method; 2) To construct a general OOTPN block for each equipment class, so the OOTPN block of each equipment in an AMC can be built easily by means of inheritance; 3) To define joint transitions to connect OOTPN blocks of all the equipments in the AMC; 4) To construct OOTPN model for AMC. The detailed modeling process is discussed as follows.

5 Group Manufacturing Resources (i.e. equipment) into Classes Equipment Material processing machine Material handling/ transporting machine Storage machine Other machine Common machine NC machine Machining centre Robot Conveyor AGV/RGV Buffer AS/RS Measuring machine Figure 1. The Classification of Equipment The equipments with similar structure and functions can be grouped into equipment classes by using O-O method. Generally speaking, manufacturing equipments can be partitioned into four classes as illustrated in Figure 1. Construct General OOTPN Block for Each Kind of Equipment Class M1 Pi M3 M5 T1 P2 T2 P3 T3 P4 T4 P5 T5 P1 M2 M4 T 1: a machine starts setting up T 2: a machine starts loading T 3: a machine starts processing T 4: a machine starts unloading T 5: a machine status is set to be idle Pi: a part arrives P1: a machine is idle P2: a machine is finishing seting up and waiting for loading P 3: a machine is finishing loading and waiting for processing P4: a machine is finishing processing and waiting for unloading P5: a machine is finishing unloading Po: a part is leaving M1: a request for setting up signal M2: a request for loading signal to loading machine M3: a response of finishing loading from loading machine M4: a request for unloading signal to unloading machine M5: a response of finishing unloading from unloading machine M6: finishing signal Po M6 Transition Resource place information place Figure 2. The OOTPN Model for a Material Processing Machine A TPN model for each equipment class is used to depict attributes and behaviors of corresponding class. Behaviors of the equipment class are mapped into transitions, and status or attributes of the equipment class are mapped into places. Directed arcs indicate the possible directions of message passing and material flow. For example, loading, unloading, and processing of a machine, are mapped into transitions T 2, T 3 and T 4 ; the attribute (idle) is mapped into resource place P 1 as shown in Figure 2. After that, input/output places in the OOTPN block are determined.

6 The OOTPN block of material processing machine is illustrated in Figure 2. For modeling material processing machines, there is a little difference between two cases: using and not using a material handling machine to carry out part loading/unloading. Places M 2, M 3, M 4 and M 5 listed in Figure 2 are not required for modeling material processing machine without using material handling machine. In Figure 2, places P i, P o, M i, M o, M 2, M 3, M 4, M 5 are input/output places. Define Joint Transitions Po Mo Po Mo Tg Tg1 Tg2 Pi Mi Conflict Decision Pi1 Mi1 Rules Pi2 Mi2 (a)no Conflict (b)conflict among joint transitions Conflict Decision Rules P1o Tg M1o P1o M1o Conflict P2o M2o Decision Rules Tg1 Tg2 Pi Mi (c)conflict among tokens (d)conflict among state places Figure 3. Diagram of Joint Transitions and Conflicting Situations An AMC in operation is a real-time system in which different manufacturing resources have to interact and cooperate. In order to coordinate activities of these resources, the concept of joint transitions is defined. Joint transition is a special kind of transition that only connects with input/output places of OOTPN blocks. It is used to describe the relationships between OOTPN blocks of different machines. States of output places are the conditions of firing a joint transition, and those of input places are the results of a fired joint transition. Joint transitions are used to connect OOTPN blocks of some machines to form a whole OOTPN model of an AMC. In the OOTPN model of an AMC, the flow of information and materials (i.e. tokens) is dependent upon the firing sequence of joint transitions. Some of the firing decisions may simply follow the specification of Timed Petri net. For example, when a part is processed sequentially by two machines, the joint transition connecting the OOTPN blocks of these two machines does not have any relations with other joint transitions as illustrated in Figure 3(a). In this case, the firing decision only depends on the attributes of its input places. Nevertheless, for other joint transitions involved in some complicated connections, there may exist conflicts among joint transitions. The firing decisions cannot be made unless some conflict decision rules (e.g. scheduling rules) are incorporated. The following is some of the most commonly encountered conflicting situations: 1) Conflict among joint transitions (a place connects with two or more joint transitions). To solve this type of conflict, the decision rule should determine which transition is to be fired. As illustrated in Figure 3(b), when two machines are all available to process a part, conflict exists

7 between two joint transitions. A machine selection rule is employed to determine which joint transition should be fired according to status. 2) Conflict among tokens (a joint transition connects with one input place which has two or more different types of tokens, only one token is required to fire that transition as shown in Figure 3(c)). To solve this type of conflict, the decision rule should be used to choose the suitable token. For example, a dispatching rule is applied to determine which job is selected to be processed once a machine has finished a job and there are several different jobs are available. 3) Conflict among places (a joint transition connects with two or more different types of places, only one type of place is required to fire that transition). To solve this type of conflict, the decision rule should be used to choose the suitable places to fire the transition. That transition can be considered as two independent transitions that connect certain types of places separately as shown in Figure 3(d). For example, a vehicle selection rule is applied to determine which part to be transported when there are two or more parts waiting for transporting. Generally speaking, the joint transitions and control decision rules describe the control strategy for the AMC. Construct OOTPN Model for the AMC The OOTPN model of an AMC is obtained by using joint transitions to connect the OOTPN blocks of all the machines in the AMC. If there are new machines to be introduced in the AMC, the OOTPN blocks for newly introduced machines can be constructed easily by inheriting from pre-defined general OOTPN blocks for each equipment class. When an AMC is reconfigured, the OOTPN model is reconfigured by easily selecting (perhaps modifying) modeling objects (i.e. general OOTPN blocks for equipment classes, joint transitions and control decision rules). After that, a performance analysis can be carried out. CASE STUDY In this section, OOTPN paradigm is demonstrated to construct a model for an AMC. An agile manufacturing cell is used to machine prismatic parts, which consists of two machining centers, one buffer, and one RGV. The layout of this cell is illustrated in Figure 4(a). The model for machining center is similar to the model illustrated in Figure 2. The models of buffer and RGV are illustrated in Figures 4(b) and 4(c) respectively. The OOTPN model for the whole cell is shown in Figure 5. In Figure 5, there are seven joint transitions in the cell model. T g1 represents parts are moving to buffer by RGV. T g2 and T g3 represents parts will be processed on machining centers 1 and 2 respectively. T g4 and T g5 stands for parts are moving to machine center 1 and 2 by RGV respectively. Firing of T g6 and T g7 means part having finished processing on machining center 1 and 2 respectively will be moved back to the buffer by RGV. If T g2 is fired, it means part will be processed on machining center 1. If T g4 is fired, it means part will be moved to machining center 1 by RGV. The relation between T g3 and T g5 is similar to that between T g2 and T g4. Therefore, control decision rules can be defined as: IF T g2 is fired THEN T g4 is fired, IF T g3 is fired THEN T g5 is fired. A part may

8 enter either machining center for processing as long as the machine is idle because two machining centers are identical. Therefore, if T g6 or T g7 is fired, part will be moved back to buffer by RGV, which means T g1 will be fired. The control decision rule can be defined as: IF T g6 OR T g7 is fired THEN T g1 is fired. MC1 MC2 MB2 PBo MA1 PAi RGV TB2 TA1 Buffer PB1 PB2 PA2 PA1 TB1 TA2 MC1 RGV Buffer MC2 MC3 Other Cell MB1 PBi TB1: part is entering TB2: part is leaving PBi: part waiting for entering PBo: part having left PB1: buffer is not full PB2: part is in the buffer MB1: a request for entering signal MB2: a response of leaving MA2 PAo TA1: RGV starts moving TA2: RGV finishes moving PAi: part waiting for moving PAo: part having arrived a PA1: RGV is idle PA2: RGV is moving MA1:a request for moving signal MA2:a response of finishing moving signal (a) Cell Layout before and after Reconfiguration (b) The OOTPN Block of the Buffer (c) The OOTPN Block of the RGV Figure 4. Cell Layout, the OOTPN blocks of Buffer and RGV Furthermore, there exists conflict between T g2 and T g4. It must be determined which machining center is used to process the part. Conflict of determining which part should be moved by RGV also exists between T g6 and T g7. Conflict decision rules solving such problems should be used according to certain objectives and constraints. For example, FCFS (First Come First Serve) or SDF (Shortest Distance First) would be adopted to determine which part is moved by RGV. When a new type of part is to be machined, this cell needs to be reconfigured. Machining this new part type may not be accomplished on the two machining centers. It might be required to add a new machining center referred as machining center 3. The part is first processed on machining center 1 or 2, and then sent to machining center 3 for the remaining finishing operations because MC3 has higher accuracy than MC1 and MC2. At the same time, MC3 is also a member of another cell. That is to say, the reconfigured cell shares MC3 with another cell as shown in Figure 4(a). The new model for the reconfigured cell can be quickly established based on the old model illustrated in Figure 5. The models for machining center 1 and 2, buffer and RGV remain unchanged. The model for machining center 3 can be derived from general OOTPN block for material processing machine, whose structure is similar to other two machining centers. The modeling task here is to establish a new model of the reconfigured cell by combining the existing OOTPN blocks through joint transitions. In the new model as shown in Figure 6, there are two new joint transitions, T g8 and T g9. T g8 represents part having been processed on machining center 1 or 2 is moving to machining center3 by RGV. T g9 represents part having been processed will be moved back to buffer by RGV. Firing of T g6

9 and T g7 means part having completed processing on machining center 1 and 2 respectively will be sent to machining center 3. Other joint transitions of the old model are used without modification. Because of machining sequence, there is a new control decision rule, IF T g9 is fired Then T g1 is fired. This rule means part must complete all operations before moving back to buffer. The rule, IF T g6 OR T g7 is fired THEN T g1 is fired, should be modified as IF T g6 OR T g7 is fired THEN T g8 is fired. It means the part will be sent to machining center 3 after finishing processing on machining center 1or 2. Tg1 Tg1 MB1 PBi MB2 PBo MB1 PBi Tg2 Tg3 MB2 PBo MA1 PAi Tg2 Tg3 MA2 PAo MA1 PAi Tg4 Tg5 MA2 PAo M11 P1i M21 P2i Tg4 Tg5 M16 P1o M26 P2o M11 P1i M21 P2i Tg6 Tg8 Tg7 M16 Tg6 P1o M26 Tg7 P2o the model for other cell Tg10 Tg11 M31 M36 P3i P3o Tg9 Figure 5. The Complete Model for the Cell Figure 6. New Model for the Reconfigured Cell It is often encountered that one machine works for more than one production tasks in a certain period. In this circumstances, for example, MC3 works for production task besides the above mentioned one. Since MC3 is shared by the reconfigured cell and another cell, two new joint transitions, T g10 and T g11, are introduced to describe this sharing relationship. This type of joint transition is called connecting transition that connects with OOTPN model of another cell. Obviously, there should be two new decision rules, IF T g8 is fired Then T g9 is fired and IF T g10 is fired Then T g11 is fired, which means MC3 works for one cell at certain moment and ensure correct material flow. There exists a conflict between T g8 and T g10. A suitable decision rule must be employed to determine which cell obtains the machining service of MC3 at the conflicting time. Status of the reconfigured cell and another cell must be considered simultaneously. If more than one machine is shared by two or more cells, more connecting transitions and corresponding decision rules should be applied. After the model is established, an effective schedule can be achieved from analyzing this model. Optimum cell configuration can be suggested based on the analyzing results as well. The case study shows that OOTPN model can be used to describe the control logic and dynamic behaviors of an

10 AMC. Its characteristic of configurable-on-demand makes this method meet the requirements of AMC modeling. CONCLUSION An OOTPN modeling technique for AMC modeling is discussed in this paper. It is based on O-O technique and Petri-nets. It can explicitly describe the control logic and dynamic behaviors of an AMC and possesses high modularity and reusability. The OOTPN modeling technique for AMC shows advantages as follows. 1) The model possesses high modularity because modeling objects (i.e. general OOTPN blocks for equipment classes, joint transitions and control decision rules) is pre-defined; 2) The modeling technique possesses high reusability because modeling objects can be reused when reconfiguring AMCs; 3) The model can be used to describe the control logic and dynamic behaviors of an AMC so as to form optimum cell configuration; 4) An effective schedule can be achieved through model analyzing. ACKNOWLEDGMENT The research reported in this paper was supported in part by the National Natural Science Foundation of China under grant number References 1.Wang L.C., An integrated object-oriented Petri net paradigm for manufacturing control systems. INI.J. Computer Integrated Manufacturing, 1,73-87, Chen K.Y. and Lu S.S., A Petri-net and entity-relationship diagram based object-oriented design method for manufacturing systems control. INI.J. Computer Integrated Manufacturing, 1, 17-28, Mize J.H. Modeling of Integrated Manufacturing Systems Using an Object-Oriented Approach. IIE Transactions, 24, 14-26, 1992, 4.Patrick K., Object-oriented methodology for FMS modeling and simulation. INI.J. Computer Integrated Manufacturing, 6, , Li, P.G., Theory and Method on performance analysis and modeling for manufacturing system (in Chinese) (HUST Press Wuhan), Dove R., Agile Cells and Agile Production. Production, 10, 16-18, Park T.Y. and Han K.H., An object-oriented modeling framework for automated manufacturing system. INI.J. Computer Integrated Manufacturing, 4, , 1997