Communication problems of expert systems in manufacturing environment

Copyright0 IFAC Artificial Intelligence in Real Time Control, Valfmcir,Spain, 1994

COMMUNICATION PROBLEMS OF EXPERT IN MANUFACTURING ENVIRONMENT

SYSTEMS

J. NACSA and G. L. KOVkS Computer ajld Automation Research Institute of Hungarian Academy oJSciences, Computer htegrated

Mamfacturittg

Research Laboratoyv, H-1518, Budapest, POB63. Hutlgay

Recently increasingly artificial intelligent methods are used in the computer integrated manufacturing (CIM) applications. Real-time expert system shells are good tools to develop intelligent cell controllers. The first part of this paper summarises the communication problems of the expert systems in the CIM applications in the level of connection point of view. Then a research application called SSQA are shortly described with its aims and modules. The third part explains the way of the communication in SSQA. Abstract.

Key Words. Simulation

Conmunication

control applications;

1. INTRODUCTION

Expert systems, Flexible

manufacturing;

An application based on an appropriate expert system shell is suitable for implementing a model of an FMS (resources, batches, process plans, capacities, etc.), it gives response for an inquiry within a few (milli)seconds, and it is able to manage the continuously arriving external data (Kovhcs et al., 1994).

The evaluation and operation of flexible manufacturing and/or assembly systems and cells (FMS in the following) are rather complicated tasks as these systems are complex with different types of possibilities and constraints. Some part-problems have correct analytical description. In the other hand many connections of the elements and attributes cannot be described by means of simple I/O equations, and often not exactly checked empirical solutions are used in the applications. So the decision support (description of facts and connections, easy programming and modification, clear usage) is a big question in these systems. A promising method is the combinations of sequential and AI programming to deal with such evaluation and control systems. Intelligent and comples decisions can be supported by espert systems in several operation steps that need (Smith et cd., 1992).

2. EXPERT SYSTEMS AND COMMUNICATION The problems system in CIM parts. One is (physical) and them.

of the communication of expert applications can be divided into two the hardware-software connection the other is the logical one among

2.1 Connection with other tasks. devices A basic problem of an expert system (ES) as a controller is to reach the environment, the control devices, other controllers. For the data transfer and communication with other tasks in the shells available in the market there are a relative easy C/C++ interface. In most cases it supports a clear and easy programming but general interface to reach objects. call procedures, set and get variables. So nearly each CIM implementation requires special

Time limits are also a critical constraint of control and supervision problems of FMSs. It was diflicult to manage the time easily in the most first generation of AI tools (LISP, Prolog based applications). Today there are so called real-time expert system shells available (G2, Cogsys, RTAC) with built in mechanism to handle time, asynchronous events, reasoning under time constraints, etc. (Laffey et ol., 1988). 243

software development to cover this gap between the outer world and the ES. In the CIM area there are more accepted model or modelling tool to describe the objects of an FMS. In the communication point of view the most promising one is the object oriented view of the so(Manufacturing Message called MMS Specification), which is original an application layer protocol in the MAP OS1 networks. MMS gives a so-called VMD (Virtual Manufacturing Device) view about each resource of the FMS. It was realised that this specification is good on the higher level of the FMS (e.g. Nagy and Haidegger, 1993) to give a communication oriented view about the network elements and their resources. It is a recent project in the CIM Research Laboratory to build in MMS into a real-time expert system.

2.1 Logical levels of communication Clearly the communication functions depend on the capabilities of the expert system. The way of learning and knowledge handling determine the logical levels of the communication. Three diKerent types of working mode of an intelligent cellcontroller in a CIM environment are shown in the Fig. l., 2. and 3. illustrated the different levels of the communication. In the Fig. 1. the easiest solution can be seen. The cell-controller has a pre-programmed knowledge and according it leads the production. It gets data (events. status, uploads) from the controllers. PLCs and send them control information (commands, downloads). The cell or area controllers eschangc only production information that are also only data for the knowledge base. In this case the communication level is only data acquisition and controlling (e.g. “Part VAL3-5 ready”, “Go AGV-I to MC-12”. “Download NC-1994-ESP”).

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The Fig. 2 shows the solution where the normal features of an expert system are built into the application. There is active knolvlcdge acquisition over the functionality of the first case. The expert system is working on to exact its knowledge base. In the communication point of view it means mainly more data acquisition. It is also needed to start special actions for getting deep knowledge.

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There is a vcq important constraint that the knowledge processing has to be lower priority than the real-time control. Most of these solutions do not allow the espert system the self learning. The human supervisor decides after the knowledge acquisition to update or not the knowledge base. The main feature of this case is the capability of the digging of hidden coherence and intelligent filtering of huge information.

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This usage of the expert system provides only a special way of information processing instead of a sequential programming. The experiences of the real applications prove that the quality of the software and the time and cost of the development are the main features of such an expert system solution.

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In most cases this level of communication is used in the real applications. There is no special problem because of the real-time control in the communication point of view. The requirements of the speed and data flow are not bigger than the normal ES interface and the communication network offer by default. Rather sophisticated but good performance information systems can be build based on a real-time expert system and any industrial networks for a given FMS.

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Fig. 3. shows a solution that may be a future application. It is a very interesting AI field how to manage kno\\ledge communication, where a system supports that another system can reach its

Fig. 1. FMS controlled by an espert system wilh permanent knowledge

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knowledge through a restricted set of communication operation. It means in the CIM field that to cell controller can support each-other with their knowledge (e.g. how to manage a special situation).

The prototype application was developed with the real data (layout, capacities, process plans, machine parameters, etc.) of the Pilot FMS of the Technical University of Budapest. In the ES the application specific and independent parts are strictly separated.

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Fig. 3. FMS controlled by an expert system with knowledge communication and acquisition capability

The three level grouping presented is only a possible classification of the communication of the ES in CIM environment. They would like to underline that the real problem is the logical communication (how to working together) while the physical one can handle with the normal communication tools and methods.

3. THE SSQA APPROACH These motivations were used in a research project called SSQA (Simulation-Scheduling-Quality Assurance). In this project (Kovacs, 1994) a simulation of an FMS was used instead of a real control. So in the communication part the logical connections had bigger importance, In SSQA a knowledge based simulation and quality assurance system was devclopcd. The system is mady for evaluating a certain FMS with different parts, orders, capacities and analysing the quality assurance aspects based on simulated measurements and SQC methods. In the SSQA system the simulation is implemented in the world-wide accepted ‘traditional’ SIMAN IV. (Cinema) simulation ~aninlation) system. It is surrounded by espert system modules (preparation, on-line advisor and preparation) implemented in the same G2 Ver. 3.0 object-oriented real-time expert system shell. The structure and the functionality of the system were designed and developed that later the simulation may be changed to a reaf FMS environment” and the ES would be a cell-controIler.

CONNECTING G2 and SIMAN

The expert system nins before the simulation {as a preparation system) and then starts it. Then the two systems are running parallel exchanging data. The ES acts as an advisor system giving scheduling and quality assurance advises. Finally the ES evaluates the results of the simulation and prepares another simulation if necessary. Because of the evaluation several experimental simulation runs are needed analysing the given FMSs with the same or modified production tasks. So the simulation has to be able to run many times during the run of G2 in such a way that the two systems should communicate with each other during each simulation run. As the espcrt system might modify some parameters of the run-time simulation software the recompilation of SIMAN so-called Esperimental frame is necessary. The m~i~cations of the orders, process-plans, quality parameters and measurements are allowed, but in this approach the basic main parts of the simulation model (etc. the resources> are permanent. So whenever the ES modifies the experimental frame (create a new version from one of its include files), the system will recompile and link again the simulation program before starting it again. Both G2 and SIMAN have further restrictions in their interfaces: - The G2 System Interface (GSI) which makes communication between Ci2 and an external system possible can initial& and establish their communication only if the external system already runs when the G2 knowledge base is started or it is in reset state. This limit means that it is not possible to build together the SfMAN and GSI C parts because the G2 wants to start the simulation. - Due to its internal entity driven philosophy SIMAN is able to com~~ullicate with other systems only as client, but GSI provide a communication where G2 is the client and the interface program is the server. There is a very important practical constraint of the SIMAN C interface that SIMAN IV. was coded in FORTRAN IV, so the possibilities to reach SIMAN

inner simulation variables and paramctcrs from its C interface arc limited or at least complicate.

corrcspondcncc was an easy task comparing lvith the interface. To meet the above rcquircmcnts the comn~un~c~tioI1is s&cd in the follo~ving \vay (Fig. 4. shows the information flo~v,.

G2 - of course - has an object oriented and SIMAN has an entity driven problem solving view. The

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Fig. 4. I~ormation flolv betjveen the SIMAN and G2 using K~~TASK

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A third (SIMAN and G2 are the first and second) task (KO~TASK~ is defined and coded in C to make communication possible between SIMAN and G2. This is connected to G2 via GSI and to SIMAN via RPC (Remote Procedure Calls) used in TCP/IP networks. KOMMTASK is a server task that is listening to either G2 or SIMAN depending on the status of the system. RPC connection allo\vs that SIMAN and G2 are able to run on dil&rent workstations.

and G2 knoiv that SIMAN has

KOMMTASK already run.

When SIMAN mants to send a message (a SinluIation event) to G2, then it is sent to KOMMTASK, what conveys it to G2 in the form of an GSI external procedure call. This procedure activates the rule-set (and other G2 parts) belonging to the message, and after the calculation of the anslver the return values (control data) are given back to KOMMTASK by calling an other GSI procedure. These values are given to the return branch of the RPC call to SIMAN by KO~ASK. The simulation does not continue its tasks until it receives the answer from the advisor. The communication and the reaction of the ES to the message are so quick, that in the animation no halt effects can be seen. So it can be said real-time in this environment.

The run of KOMMTASK starts first to be able to initialise GSI at the start of G2. Also in the initialisation phase KOMMTASK starts its RPC server towards SIMAN because SIMAN \vill communicate as the RPC client. When G2 lvants to start SIMAN it puts the KOMMTASK into RPC server lvaiting status, and the SIMAN task is started by means of a UNIX shell call, and not via KOMMTASK. This task (SIMAN) activates the RPC connection lvith KOMMTASK during itself initialisatlon. From this first RPC call

The SIMAN task is aborted \vhen the simulation run is over and such a message is given to KOMMTASK. KOMh4TASK conveys this message

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to G2 and waits for a new GSI message from G2 and stops waiting for SIMAN RPC messages.

simulation of them with real-time expert systems implemented in expert system shells. Expert systems can help in more effective, more reliable production. This paper gave account on some results and experiments of using interfaced ‘traditional’ and espert systems.

The final status of the tasks and their communication allows G2 to start a new simulation whenever it wants.

The performance of the present SSQA is better than it was expected mainly because of the speed of the communication.

When the whole session is over the KOMMTASK and the G2 tasks are stopped. As KOMMTASK is a server towards two different systems using different protocols, the routines of most embedded RPC server had to be modified. As the response-time of G2 is non-deterministic an increased RPC time-out had to be used. It was important in the developing phase.

The nest work is to create a standard interface between industrial network protocol (MMS) and expert system (G2), that allows the usage of SSQA results in the real-time control applications.

implemented Ill discussion of the the communication it must be mentioned that it has no part that depends on the given FMS. A new application requires only two things. It has to be built up a similar structure SIMAN model for the other FMS and to be created the new instances of the G2 FMS objects according to the new problem. The main part of the G2 program, and all parts of the interface things would be the same.

6. REFERENCES Laffey, T.J., P.A. Cos. J.L. Schmidt, S.M. Kao and J.Y. Read (1988): Real-time knowledge based systems. .4ZMaguzine, 9, 1, 2745. Kovacs. G.L., J. Nacsa, D. Gavalcova (1994): A knowledge based and a hybrid system to evaluate flexible manufacturing systems, Proceedings of the I991 IEEE international Conference on Robotics and Automation, 3570-3575, IEEE

In the first version of SSQA all the tasks are running on a SPARCstation 1+ (20 Mbyte RAM) under SunOS 4.1.1. An additional X terminal is used for to appear parallel the G2 messages and user menus and the animation (Cinema).

5. CONCLUSIONS,

Computer Society Press, Los Alamitos Nagy, G., G. Haidegger (1993): Object oriented approach for analysing, designing and controlling manufacturing cells, Conference Proceedings of the d UTOFACT’93. 12-l - 12-10, Society of Manufacturing Engineers, Dearborn Smith. P.. E. Fletcher, M. Thourne, W. Walker, K. Maughan and M. Hajsadr (1992): The use of expert systems for decision support in with system Expert manufacturing, applications, 1, 1 l-l 7.

FUTURE PLANS

A basic problem of using artificial intelligence means, as e.g. expert systems for real time control of manufacturing and assembly systems is to solve interfacing the industrial systems and networks or a

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