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Expert Systems With Applications, Vol. 8, No. 2, pp. 249-254, 1995 Copyright © 1994 Elsevier Science Ltd Printed in the USA. All rights reserved

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ESPANDA: For Solving Problems by Applying the Principle of Similarity

ANGELIKA GARBEN, MICHAEL FI~RNSINN, AND BERND RUSCHKOWSKI

INPRO, Berlin, Germany

Abstract--ESPANDA is a software system that was developed for consultation on problems in connection with machining operations. The prototype o f the system was implemented by INPRO ~ in collaboration with Mercedes-Benz AG and Volkswagen AG. The approach taken in this system is based on the assumption that problem classes, or so-called similarity classes, can be defined for machining problems. The solution to a given problem is derived inductively with the aid o f a library o f case data. This approach will be explained in greater detail within the context o f the problem area in question.

1. THE PROBLEM CONTEXT

MACHINING OPERATIONS, for example, turning, drilling, and cutting, occupy a key position in production (Spur, 1979, p. 7). Due to steady advances in the development and improvement of materials, tools, and machines, moreover, the demands made on those re- sponsible for planning the manufacturing equipment and processes are continually growing. High tool costs and expensive machines are making it vital to identify the optimum machining conditions in each case. By switching to new cutting materials--for example, from high-speed steel to carbide--and taking advantage of the faster rotational speeds of new machines, the av- erage output values can be increased and production times thus shortened.

Other restraints on the possibilities for shaping the production process must also be taken into account. For instance, other areas of corporate activity like cost calculation, ordering, purchasing, and controlling continually impose new restrictions that can have technological consequences. Because of environmental regulations, dry machining is gaining in importance over wet machining. In these areas, new experience must be continuously gained and made widely available.

Requests for reprints should be sent to Angelika Garben, INPRO, Nuernbergerstr. 68/69, 10787 Berlin, Germany.

INPRO--Innovationsgesellschaft f'tir fortgeschrittene Produk- tionssysteme in der Fahrzeugindustrie mbH in Berlin is a jointly owned subsidiary of Mercedes-Benz AG, Krupp Stahl AG, SIEMENS AG, Voest-Alpine Steinel Ges.m.b.H., Volkswagen AG, HOECHST AG, and the Berlin Senate.

249

This article is not intended to take account of all of the problems of this highly complex area within the framework of the ESPANDA project. Instead, a relevant and straight-forward area in connection with technological aspects was defined (Figure 1) with the aim of obtaining useful and informative system output.

The already existing aids within this technological context have revealed themselves to be of little practi- cal use.

The quantitative links between technological parameters and machining results cannot be adequately captured in their entire complexity by analytical, mathematical models. Particularly because of the dy- namic nature of technological progress in the field of machining, such models cannot be modified quickly enough to take new developments into account. Be- cause they have been intentionally designed to provide generalized solutions, moreover, they are less suited for dealing with specific problems.

The values recommended by tool manufacturers can only serve as general guidelines. They are virtually never applied in practice, because they do not (and cannot) give consideration to the special conditions existing in a given manufacturing operation: for example, the varying quality of cutting and other tools made in-house and by different manufacturers, of cy- cling times and tool-changing intervals, and so forth.

Similarly, the existing data bases open to access by the public (e.g., INFOS in Germany, METCUT-MDC in the United States, and TRI in Japan), which were set up to support planners, also tend to contain only generally valid values, being unable to take account of plant-specific parameters. The data stored there are from machining laboratories or industrial production.

250 A. Garben et al.

Standards Purchasing Costs

M a n u f a T a m Further processing

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Users criticize that these data cannot be checked, and their reliability is often called into doubt. There is also little or no support in the form of on-line availability or utilities that would permit direct utilization of data base resources for solving specific production problems.

Here, the knowledge that machining experts have accumulated through many years of experience-- in any case with tried-and-proven technologies--mainly provides the basis for deriving good solutions to machining problems. It generally takes 5 to 6 years for a beginner to become a machining expert. This means that even normal personnel fluctuations can signifi- candy reduce the availability of this knowledge, making it all the more desirable to conserve it.

2. T H E P R O J E C 1 ~ O B J E C T I V E

The objective of our project was to place a tool at the disposal of production planners that would enable them to solve the problem of how to optimally allocate equipment and fix machining values (Figure 2).

From the large range of different machining operations, drilling was initially selected to be concentrated on. This operation has been largely ignored in the past, and there is consequently a large information deficit here. The problem at hand is sufficiently complex to serve as a test for validating the new approach.

To this end, it was planned to incorporate results that had already been obtained. These results consisted

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ESPANDA and the Principle of Similarity 251

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of information on machining cases that had been captured but not yet written down or entered into in a data base. Thus, one of the principal subtasks involved in the project was to compile a case library (Figure 3). The primary goal of the project was to identify a pro- cedure that would permit the machining records collected in the case library to be utilized as directly as possible for dealing with new production tasks.

In addition to its main function as a consultant, moreover, the system-- i f used and maintained regu- l a r ly -a l so contributes to solving the problems asso- ciated with the fact that machining knowledge is nor- mally linked to specific individuals and therefore is only temporarily available to a company.

3. T H E S O L U T I O N

The approach selected for ESPANDA involves search- ing a case library to find solutions to similar problems in the past. This approach departs from the assumption that problems can be collected together into classes, and that it is technologically feasible to apply the same solution to problems of the same class. The problems collected together in such a class are regarded as being similar to one another (thus forming a "similarity class"). Machining cases involving similar problems thus contain potential solutions (Figure 4).

The first prerequisite (Hart, 1986, p. 110) that must be met in order for this approach to work is the existence of a collection of machining records (case library). As used here, the term "machining record" refers to a description of a machining case, consisting of three parts: • Description of the problem. • The solution found for dealing with the problem; in

the case of drilling, that means descriptions of the characteristic attributes of the machine and the tool

used for a given drilling operation, as well as of the settings employed.

• The results of the machining operation, for example, the achieved machining quality, the service life of the tool used, and so forth, all of which can be used to assess the limitations of the solution.

The second prerequisite that must be met is classification of the possible problems. This classification constitutes the link between a given problem and the case library. Since the comparisons to establish similarity make use of the problem description, it follows that the problem descriptions contained in the machining records must at least contain all attributes that are relevant for purposes of comparison.

For the field of application addressed by ESPANDA, the experts confirmed that the parameters chosen for purposes of comparison can be regarded as being in- dependent of one another. Consequently, it was possible to define the problem classes by classifying the value ranges for their individual attributes. By means

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of this intentional description of classes by applying formative laws, we were able to avoid explicit representation of these classes. This was very important for the success of the project, since otherwise it would have been necessary to describe more than 30,000 classes, even in our already-narrowed field of application.

In this approach based on the principle of similarity (Figure 5), the problem class corresponding to the problem at hand is first identified. Because the problem classes were intentionally described, that means that an explicit representation of the class to which the problem at hand belongs must be generated. This is achieved by determining the characterization class corresponding to the characterization of each attribute of the problem. In the case of metrically defined attributes, for example, these characterization classes can simply contain intervals. For other attributes, same- ness--a special instance of similarity--is required. Here too, it is possible to speak of characterization classes, if the reference value is treated as a class containing just one element.

After generating the problem class, this is used as an argument for a search of the case library. The result of this search is a set of machining records with similar problems. It is quite possible that the search will turn up an empty set. In this case, the ESPANDA user still has the possibility of "softening" the search argument by systematically extending the characterization ranges defined by the problem class.

If the search has been successful, then an attempt is made to process the resulting set of cases in such a way that it can be directly applied to solving the problem at hand.

The approach used in ESPANDA can be regarded as an extension of existing data base systems (Figure 6). In order for such a case data base to be utilized, users must first formulate a suitable database inquiry and then interpret the results of the inquiry in order to apply them to the specific machining problem in question.

Identifica~arch ~stta~on

Formulation ~ ~ P~rgqm~qOf the results

FIGURE 6. Utilization of a data base to find the solution to a problem.

Neither of these problems is trivial, however. Users of such a case data base must be able • to recognize what they are looking for; • to tell which cases are similar; • to formulate an appropriate inquiry; and • to interpret the results of the inquiry for application

to the problem at hand. Moreover, such capabilities are present in varying de- grees in different users. As a result, the benefits derived from using such a database depend to a large extent on who uses it. ESPANDA specifically addresses these weaknesses: • Users enter their machining problems directly into

the system. • ESPANDA already contains a definition of "simi-

larity." • ESPANDA automatically formulates the search in-

quiry. • The system then also automatically adapts the results

of the search to the problem situation. Thus, ESPANDA lets users proceed from a given

machining problem to a directly usable proposed solution (Figure 7). The responsibility continues to rest with them, however. All usable material is made available in order to enable users to evaluate the proposed solution and to modify it as required. Even in the event that no relevant machining records are found in the library for a given production problem, ESPANDA

FIGURE 5. The approach to finding solutions. FIGURE 7. Use of ESPANDA to find the solution to a problem.

ESPANDA and the Principle of Similarity 253

grants users easy access to the stored machining records by "softening" the stipulated similarity criteria, so that they can at least obtain useful information to help solve the problem.

In order to appreciate the advantages of this induc- tive approach, ESPANDA should be compared with purely deductive approaches (Bochenski, 1954, p. 75). The principal differences are in the effort required for knowledge acquisition and modelling. These only pay off if the prerequisites for use of the approach are met, and if the system is applied to a sufficiently complex field in which several years are required to collect the needed experiential knowledge (Hayes-Roth, 1983, p. 43): • Specialists find it easier and less time-consuming to

collect and structure case data than to formulate all of the interdependencies and cross-links involved. Consequently, better account is taken of the time- critical demands placed on the specialists.

• The complexity of the knowledge that must be culled and modelled is greatly reduced. When we break a machining case down into problem, solution, and result, it becomes apparent that knowledge acquisition and modelling can essentially concentrate on the problem itself. For instance, it is automatically no longer necessary to inquire about the conditions under which a certain tool type or cutting material combination cannot be used, since there will not be any reference cases containing this combination.

• The readability and thus also the serviceability of the system that has been developed are greatly favored by the approach taken. It is easier to read examples in conjunction with a few useful pieces of information, even if they were written by a stranger, than to read complex statements about conditions and consequences.

During the course of the project, other positive side- effects also manifested themselves: • The uniform documentation and structuring of

problems and solutions automatically lends support to systematization efforts. A basis for standardization activities is established.

• The abstracted view of the stored data enables users to develop a superior grasp of the range of technological possibilities and of the general principles of the field at hand.

• The joint structuring work done within the project team leads away from a division of labor between developers, on the one hand, and users, on the other; instead it stresses the more constructive nature of teamwork for elaborating knowledge-based concepts.

The drawbacks of our approach have to do with its dependency on the availability of data. The quality of the results output by the system depends crucially on the quality and quantity of case data available. The

body of data must therefore be intensively maintained. If one relies excessively on interrelations extracted from the case data for identifying the similarity classes, one runs the risk of winding up with incomplete knowledge, since it can be assumed that the case data neither ex- haustively cover all possibilities, nor will they consis- tently be of opt imum quality.

The prerequisites that must be met in order to apply this approach can thus be summed up as follows: • Attractiveness of the approach: To solve their prob-

lems, the specialists already look up already solved cases, or regard such an approach as sensible.

• Feasibility assessment by the specialists: The specialists regard it as feasible to verbally group problems into replicable classes.

• Existence of case data: There are completely des- cribable, solved problems in the factory. These should be sufficiently extensive in scope to provide an overview of the full range. The existence of these data do not necessarily mean that they have been documented. It may be that they are in actual practice or can be easily generated, but would still have to be captured.

• Limited nature of the problem: Similarity-based descriptions can only do justice to a single aspect (e.g., the technological side, the economic side, etc.). When various aspects are looked at, therefore, they must be sequentialized.

Certain advantageous conditions favor the successful implementation of a project: • The number of problem parameters should be on

the order of about 10. • In order to support definition of the problem classes,

the solution space must be structured. For this purpose, it is advantageous if the solution space can be subdivided into a small number of fundamentally differing solution branches.

4. I M P L E M E N T A T I O N

The system was developed in two stages. In the first step, the initial aim was to permit a well-grounded feasibility analysis and, at the same time, as complete as possible a definition of the requirement profile. To that end, a fairly extensive prototype was produced for drilling. Following on from an intensive testing phase of the prototype by the pilot users, the second step involved the implementation of a basic version of the productive system. This involved a complete re-implementation, the aim of which was to take account of the requirements resulting from the pilot test.

The prototype system for drilling operations was implemented on a SIEMENS WS 30/ApoUo DN 4000, using the LISP-based development tool KOBRA, which was developed at INPRO, and the data base system ORACLE.


The knowledge representation language of KOBRA provides elements (classes and instances) that can be used to describe the objects of the problem area, their attributes, and the manifold relations linking them. An additional representation scheme (rules and rule structures) is available for depicting the knowledge for solving problems, that is, knowledge about possible new aspects and possible changes of different aspects in certain situations. An interface (KODA) to the relational data base system ORACLE has been implemented in KOBRA. This interface can be employed to access existing data, and also permits KOBRA object structures to be stored. Any required data base inquiries are automatically generated by KODA. For this purpose, it is merely necessary to describe a mapping of table col- umns in the database to object attributes.

In the second step, the productive system was developed. After a concluding study of the prototype developed as an exploratory model and taking into account real productive conditions, the system was re- implemented by SIDATA on a 486 PC, based on UNIFACE.

5. STATUS AND O U T L O O K

The prototype system has been in use since October 1991 at Mercedes-Benz AG, Volkswagen AG, and SIEMENS AG, the productive system since June 1993. It has met with a high degree of acceptance, and the specialists have confirmed the effectiveness of this approach. The extension to cover turning operations has been worked out as a concept, and it would appear to be worthwhile to follow a selective introductory phase for the productive system.

REFERENCES

Bochenski, I.M. (1954). Die zeitgenOssischen Denkmethoden. Tiib- ingen: Francke Verlag.

Hart, A. (1986). Knowledge acquisition for expert systems. London: Kogan Page.

Hayes-Roth, F., Waterman, D.A., & Lenat, D.B. (1983). Building expert systems. Reading, MA: Addison-Wesley.

Spur, G., & St6ferle, Th. (1979). Handbuch der Fertigungstechnik, Vol. 3.1; "Spanen "'. Munich and Vienna: Carl Hanser Verlag.