Intelligent Manufacturing Systems: amethodology for technological migration

Jorge Gamboa-Revilla and Miguel Ramırez-Cadena. ∗

Abstract—More complex products in efficiency andquality, the necessity of diminish energy spending andinvestment reduction; amongst other things, are dis-turbances, and their occurrence may have severe im-pact in the performance of actual manufacturing sys-tems. Manufacturing systems should be based in dis-tributed and autonomous entities, being possible theaddition of new components without stopping or re-starting processes. All these facilities point to theconcept of agile manufacturing systems. The ap-proach is addressed to encourage the usage of holonicand multi-agent concepts in traditional productionlines, with a friendly software upgrade and a min-imum cost in hardware expansion. A methodologythat includes the technological migration from a es-tablished flexible manufacturing structure (FMS) tointelligent and reconfigurable manufacturing system(RMS) is presented. An example of implementationwill be described in depth to show the viability of theproposed schema.

Keywords: Computer integrated manufacturing, Dis-

tributed control, Holon, Intelligent manufactuting sys-

tems, Multiagent Systems, Parallel architectures, Par-

allel processing, Reconfigurable Manufacturing Sys-

tems

1 Introduction

In the last twenty years manufacture concepts have hadseveral redefinitions, in the eighties, the concept of flexi-ble manufacturing systems (FMS) was introduced to de-velop a new family of products with similar dimensionsand constraints. But nowadays, the capacity of recon-figuration has become a major issue for improving thefunctioning of industrial processes. Indeed, today a mainobjective is to adapt quickly in order to start a new pro-duction or to react in a failure occurrence [1]. Intelligentmanufacturing systems (IMS)[2], has both flexibility andreconfigurability, in fact this concept brings more thana few ideas of software intelligence meanings, which con-templates characteristics such as autonomy, decentraliza-tion, flexibility, reliability, efficiency, learning, and self-regeneration, all of these facilities lead to the concept

∗Jorge Gamboa-Revilla, Miguel Ramırez-Cadena, Tecnologicode Monterrey (ITESM)Campus Monterrey, Mechatronics and Au-tomation Department, Av. E. Garza Sada 2501, 64849, Monterrey,Nuevo Leon, Mexico. (a00778197, miguel.ramirez)@itesm.mx

of agent-based manufacturing systems. An agent is acomputer system that is situated in some environment,and that is capable to act in an autonomous way in thisenvironment in order to meet its design objectives. In-telligent agents are able to perceive their environment,and respond in a timely fashion to changes that occurin it in order to satisfy their goals, this characteristic iswell known as reactivity. However an agent is also proac-tive, for it agent is able to exhibit goal directed behaviorby taking the initiative. In addition, agents are social,having the ability to interact with other agents [3]. Itworth to remember the definition of an ”Holon”, whichits similarities with agent definition, brings up controver-sial meanings, nevertheless an holon is well recognized onmanufacture applications with the distinctive of a morespecific intelligence use, while an agent could have dif-ferent levels of intelligence such as logical, reactive, lay-ered or in a more advanced way, with beliefs, desires andintentions (BDI)[4]. The word ”holon” comes from theGreek holos that means whole, with the suffix on which,as in proton or neutron, suggests a particle or part. Asystem of holons that co-operate to achieve a goal or ob-jective limited by rules of interaction is called holarchy[5]. On the past decade researchers have focused theirinvestigations in the theory and design of holonic manu-facturing systems (HMS), wherein can be found two prin-cipal aspects that at present are still being depurated.On one hand we have issues associated with the develop-ment of multi-agent systems (MAS), on the other handhow the MAS can be effectively deployed into manufac-turing environments [6]. In spite of having a completeset of agent architectures and algorithms, they still donot have the strength to displace established manufac-turing systems, even though the companies know that ina brief time market will change and some actions haveto be taken. This paper presents a novel approach tomanufacturing floor control design with agent coordina-tion, including the interaction through a manufacturingexecution system (MES) with manufacturing planninglevel (See fig.1) structure taken from previous researches[7, 8, 9]. This scheme uses commercial software that in-cludes a few mainly distinctive characteristics, such asblock oriented programming, parallelism for distributedstructures, and the flexibility to scale platform capaci-ties without missing the structure concept. This articlewill refer to a multi-agent manufacturing platform imple-

Proceedings of the World Congress on Engineering 2008 Vol IIWCE 2008, July 2 - 4, 2008, London, U.K.

ISBN:978-988-17012-3-7 WCE 2008

Figure 1: Floor control design with holonic coordination,including the interaction through a MES with manufac-turing planning level.

mented at Instituto Tecnologico de Estudios Superioresde Monterrey (ITESM) as a general study case; neverthe-less a methodology to convert conventional manufacturesystems into new intelligent manufacturing, flexible andreconfigurable concept shall be explained in detail.

2 The feature of migration

Since the multi-agent technology has been recognized asa key concept in building a new generation of highly dis-tributed, intelligent, self-organizing and robust manufac-turing control solution, the traditional concept of manu-facturing systems, has become vulnerable to changes [10].Environmental changes, failure detection, reconfigurabil-ity, and expandability; are a set of capabilities that makean attractive option the application of this feature of mi-gration. The idea of a standard software platform in-cluding characteristics such as reconfigurability, flexibil-ity and ”holonic-ready” [9] concepts, is justified by thenecessity of uptake on established systems, making eas-ier to adopt new production infrastructures without dra-matic hardware changes and long setup times. At themoment it is possible to find several topologies of manu-facturing cells, such as centralized, hierarchical, and het-erarchical structures [11]. Each topology could be con-sidered as optimal and able to accept migration, takinginto account that each block should be related withoutcomplete dependency, at least after migration is imple-mented, and well functioning shall not be compromisedwith any other element from the cell. A generic plat-form was designed in order to apply multi-agent schema[9]. The platform design was implemented in such a waythat any flexible manufacturing cell could be evolved intoagent-based structure. The clue is to adopt the platformstructure, and shape each element (robots, numeric con-trol machinery, conveyors) of a cell to acquire agent per-

sonality. Once the problem or problems are identified theMAS design phase, starts, which is more oriented towardthe implementation of the generic platform; however amethodology should be committed. The methodologyincludes definition of: 1st stage; capabilities of each sin-gle agent, and the inter-agent communication, 2nd stageMAS architecture planning.

3 The Methodology

Before any attempt can be made to implement agent so-cieties effectively in a manufacturing system, an analy-sis of the industrial life cycle is pivotal. It therefore be-comes important to introduce the environment in whichan agent should act [12]. For it the information systemof a manufacturing enterprise is crucial to be recognized,in order to clearly sketch how agents can be integratedand how data would be interchanged (See fig. 2), whereinthe three layers, that computer systems in manufactur-ing management use, are illustrated. The generic plat-form is toward from general to particular application, sobefore start working on developing intelligence, is crucialto make independent each element, which is supposedto emerge from a centralized and sequential architecturethat actually shall be substituted by the new platform.

Figure 2: Hierarchy model of communication and inter-action

This section will start taking the hierarchy presented onfigure 1 and model presented on figure 2, taken from ear-lier works on this research [7, 8, 9]. The superior part ofthis pyramid is performed by management layer, whichare satisfied with a manufacturing planning level, anda manufacturing execution level. Both could be pro-grammable holons, purely software based. In additionpyramid bottom is formed by executable holons, whichhas direct contact with machinery, and hardware systems,also this part of the pyramid frequently is the one withmore constraints in manufacturing environments. Theefforts on this section will be driven to get the pyramidbase prepared to be adapted without neither hardwarechanges nor design, on the other hand ready to become

Proceedings of the World Congress on Engineering 2008 Vol IIWCE 2008, July 2 - 4, 2008, London, U.K.

ISBN:978-988-17012-3-7 WCE 2008

reconfigurable, and holonic-ready [9], the methodology(See fig. 3)shall be explained as follows.

• Define Communication Structure: This answers theissue, how data acquisition will be performed andhow data shall be shared between items on internaland distributed network. On actual systems, thisis an easy matter due the well-known kinds of com-munication protocols. Data interchange is availablein several ways, for example ActiveX, data libraryfunctions (*.dll), OPC and data sockets, and thoseat the same time use different well established chan-nels of communication, such Control-Net, Profibus,Ethernet, Device-Net, amongst others, making eveneasier this step on the methodology.

• Isolate from global system: The well or bad func-tioning of one element should not affect to the otherelements functioning. In other words is essentialto rupture dependences. Isolating, could be a dif-ficult part on this methodology, understanding thateach element in traditional systems is related witha strong sequential logic, using strategies such asFirst-in-first-out (FIFO), Earliest Due Date (EDD),Shortest Processing Time (SPT), and Least SlackFirst (LSF), all of them has an acceptable perfor-mance and their use have solved many industrial op-timization problems, nevertheless are sequential de-pendant, sequential operations, and dependency arerigid and that structure is not compatible with theideal holonic infrastructure.

• Convert from general to particular: Scalability andgeneric features are the main topics on this method-ology; we must conserve generality, thinking in ad-vance to future hardware or software changes. Rigidand dedicated operation should be eliminated, toachieve different applications, making able to changeits functions.

• Create relationships but not dependences: Elementsshould be able at the end to share data, hence isnecessary to establish a weak relation with messagingprotocols such as FIPA or contract Net, that with fireactions in order to perform an application. It followsthat relationships must be created without missingcomplete agent-based structure.

The result after this methodology would be what we calla ”holonic-ready agent” (HRA), which meaning contem-plates an entity with characteristics and attributes nec-essaries to adopt intelligence blocks (to become a Holon)in order to achieve different functions or tasks. An over-all view of the resulting platform (See fig. 4) emergesfrom figure 4, where is shown in a more oriented way themethodology applied on the commercial software used todevelop the generic platform. The methodology makes

Figure 3: The methodology model implemented toachieve migration feature

possible reconfigurability into the manufacturing cell, andat the same time the cell becomes ready to adopt sec-ond stage of the problem, Multiagent architecture selec-tion. An to explain how reconfigurability is done, therobot routine, which contains ethernet procedures and*.dll functions to perform actions such as movements orexecution of a specified routine. Lets imagine that wehave to plug another identical robot to the cell, followingthis methodology procedure, it is just matter of duplicat-ing robot cycle and change some kind of IP address toachieve plug and produce, like some authors have defined[13]. Holonic or intelligent agent skills and knowledgeshould be attached to the inputs and outputs of thoseisolated cycles, the intelligent entities could be developedon different coding resources, such as Matlab, C++ orJAVA, and these can be encapsulated as an external codenode in such a way that they can be used on LabViewinterface.

4 Agent Architecture for generic plat-form

Before continuing with the study case, is fundamental todefine some aspects about the agent architecture imple-mented, remember this refer to the second phase of mi-gration problem. An agent could be categorized in purelyreactive, in which do not consider historical data to re-act, and agents with state, this category contemplatespast events, and contain internal states that describe theagent current situation, its perception of the world mapto a set of possible actions to react in different manners.However these aspects still do not clarify how functioningmight be implemented, functioning classes could be log-ical, reactive, intentional flexible (BDI) and layered, forthis application case reactive agents is selected, in whichdecision making is implemented in some form of direct

Proceedings of the World Congress on Engineering 2008 Vol IIWCE 2008, July 2 - 4, 2008, London, U.K.

ISBN:978-988-17012-3-7 WCE 2008

Figure 4: The distributed platform cycle design and mes-sage structure.

mapping from situation to action. For this implementa-tion is necessary to create three different sets or vectors,in order to define the agent structure and its virtual en-vironment. Equation 1 denote a representation of a setof discrete states E, which we can justify by pointing outthat any continuous environment can be modeled by adiscrete environment to any desired degree of accuracy,on the other side we have Ac being a set of discrete ac-tions. The basic model of agents interacting with theirenvironments is represented on equation 2, as can be seenr is a sequence with actions firing states, hence equations3 and 4 are sequence terminated by actions or states re-spectively. The environment starts in some state, andthe agent begins by choosing an action to perform onthat state.

E = s0, s1, ..., , su + 1;Ac = α0, α1, ..., , αu + 1; (1)

r : s0 ⇒α0 s1 ⇒α1 s2 ⇒α2 ... ⇒αu su + 1 (2)

RAc : s0 ⇒a0 s1 ⇒a1 ... ⇒au (3)

RE : s0 ⇒a0 s1 ⇒a1 ... ⇒au su + 1 (4)

As a result of this action, the environment can respondwith a number of possible states. However, only onestate will actually result, and obviously the agent doesnot know in advance which it will be. The rules thatgovern environment are established by the state trans-former, equation 5, at the same time each agent is de-fined by equation 6, in which an agent receives a run orsequence terminated by a state, an agent should map thissituation to an action[3].

τ(RAc) : RAc ⇒ ℘(E) (5)

Ag : RE ⇒ Ac (6)

Although architecture is designed by these abstract mod-els, the following pseudo code, represent in a very generalway how these models are implemented and the studycase is developed:

1. function action(p:P):A2. var fired:(R)3. var selected:A4. begin5. fired:={(c,a)|(c,a)~R and p~c}6. for each (c,a)~fired do7. if !((c’,a’)~fired such that (c’,a’)-<(c,a))8. then return a9. end-if10. end-for11. return null12. end function action

Thus action selection begins by fires computing the setfired of all behaviors that fire (line 5). Then, each be-havior (c, a) that fires is checked, to determine whetherthere is some other higher priority behavior that fires. Ifnot, then the action part of the behavior, a, is returnedas the selected action (line 8)[14].

5 The ITESM manufacturing cell

The laboratory installed at ITESM, consist of two identi-cal cells equipped with one loop belt-conveyor, one robot(Motoman UP6), one ASRS (automatic storage retrievalsystem) installed in a warehouse of 2x12 storage slots, aCNC machine(EMCO PC MILL 155), and an assemblytable for each cell (See fig. 5). The conveyors have threedocking stations: robot, inspection and storage station.When a raw material is introduced by an operator pro-duction orders are delivered, so that each module is awareof their tasks and roles on production. When batches ofraw material are deposited onto the belt-conveyor (Con-veyor agent), it must be aware at any time of which tasksare designated to each pallet that is navigating on theconveyor, and depending on the assigned task it can stoppallets at different docking stations in order to execute aprocess. When raw material is stopped at robot dockingstation, it could be delivered to CNC machine or assem-bly buffers, these tasks are performed by the manipulator(Motoman UP6). What to do and when has to be done,are examples of the information that order agents dealwith the cell, specifically to those executable agents incharge of that area or cell section.

6 Validating reconfigurability and agentimplementation

Previous to this section, the ITESM manufacturing cellwas described in detail in order to make a global viewof how elements are initially set and how original opera-tional flux is performed in a traditional environment. In

Proceedings of the World Congress on Engineering 2008 Vol IIWCE 2008, July 2 - 4, 2008, London, U.K.

ISBN:978-988-17012-3-7 WCE 2008

Figure 5: Layout of ITESM Flexible Manufacturing Cell.

this section the configuration of the cell will be alteredphysically in a non-dramatic way to ensure reconfigura-bility after implementing the ”Holonic-ready” platform,also two elements with no previous interaction will haveto cooperate in order to achieve a common goal. It isessential to avoid long setup times, extra physical wiring,or extra monetary investments, to demonstrate the fea-sibility of migration. The study case begins with somephysical changes, figure 5 shows the layout of the ITESMcell, for this application the camera from inspection sta-tion will be attached to robot assembly table, its imageprocessing shall construct the perception of robot agent,in other words camera should be the medium that makesthe robot able to observe its environment, whereas therobot agents performs decision making process (See fig.6). The tasks are defined as follows; raw material is de-

Figure 6: Multiagent abstract architecture for study case.

livered by an operator, and this material is formed bya pallet with geometrical figures, as shown in figure 6,

these figures do not always conserve same patron of plac-ing, thus the robot should perceive by the camera currentstate from environment, then the robot performs an spe-cific routine or action to deliver each figure to anotherpallet with a specific location for each geometrical fig-ure (See fig. 7). The petri net demonstrate on figure 7,

Figure 7: A petri net for dinamic study case representa-tion.

how actions and states modify environment from agentperspective, in a dynamic comportment. How often therobot agent performs a determinate route or path is es-tablished by the utility each path pays. The amount ofutility given for each figure could be assigned by program-mer. Nevertheless an agent always tries to maximize theutilities that it can obtain from a task [4], equation 7.

Agopt = argmax(AgεAG)

∑

rεR(Ag,Env)

u(r)P (r|Ag, Env)

(7)

Figure 8: Real working of robot and camera, results.

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ISBN:978-988-17012-3-7 WCE 2008

7 Conclusions and future works

The physical changes were successfully achieved, thereare several ways to qualify this characteristic such as timeand hardware adaptation, even both aspects were opti-mized with the usage of the holonic-ready platform, ifthere were dimension changes on assembly table for ex-ample, collision of work space would be also an importantproblem to solve, nevertheless collision could be avoidedadding some extra collision avoidance algorithms, it al-ways will depend on how old-fashioned integrated sys-tems are in the FMC to migrate and its ability to inter-act. In other words solutions for different elements, willdepend on how flexible or communicable they are, as aresult we could have several solutions. However prepara-tion of a generic platform that actually could adopt differ-ent solutions seems the most urgent issue. The platformshows sufficient flexibility to accomplish the unexpectedrequest of assembling products, as well as showing flexi-bility in removal, addition and reconfiguration of assem-bly devices. We could succeed to implement a holonic-ready platform in a generic mode showing its capabilityfor migration to convert common FMSs into RMS agent-based systems. As future work a scheduler of assemblydevices shall be developed, and interaction with superiorlevels such manufacture execution system, both have tobe developed in a generic schema to be adopted on theplatform, opening different research lines such as logisticsand planning for intelligent manufacture, and technolog-ical migration.

8 Acknowledgements

This work is a contribution for the Rapid Product realiza-tion for Developing Markets Using Emerging Technolo-gies Research Chair, Registration No. CAT077. The au-thors wish to acknowledge the support of the Tecnologicode Monterrey, Campus Monterrey, Graduated Programdepartment and the Mexican Science and TechnologyCouncil (CONACYT) sustaining under Grant J110256.

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Proceedings of the World Congress on Engineering 2008 Vol IIWCE 2008, July 2 - 4, 2008, London, U.K.

ISBN:978-988-17012-3-7 WCE 2008