Module-based intelligent advisory system

Int. J. Production Economics 60—61 (1999) 195—201

Module-based intelligent advisory system Jyri Papstel* Tallinn Technical University, Ehitajatee tee 5, 19086 Tallinn, Estonia

Abstract In this paper the principle of advisory system architecture applicable to part manufacturing systems is introduced. The system is object-oriented and two-level functioning, hence it consists of problem modules: design, manufacturing, etc. This means that the user can select the necessary configuration of the system. Decision-making methodology as a part of the system is described as well. Also, some prototypes of the modules are introduced.  1999 Elsevier Science B.V. All rights reserved. Keywords: Intelligent advisory system; Decision-making

1. Introduction Nowadays, engineering environment is characterised by the following features: E rapid involvement of newly engaged engineers with little experience in practical activities in machine building and the metalworking industry; E virtual team technique instead of personal task solution; E a lot of new design and process planning supporting systems without reliable information in the field are in the market; E crucial need in competent information by small and medium sized enterprises in various fields;

* Tel.: 372 620 3260; fax: 372 620 2020; e-mail: jpapstel@ edu.ttu.ee.

E excess of information sources; E variety in choice and a great number of choice criteria; E a possible subjective factor in connection with setting up of criteria. A number of published works e.g. [1] exist in the field, no advising system has been presented explicitly. Such a system is in the process of development and will be introduced in this paper.

2. Architecture of the system Main demands of the system: E information, used and supplied by the system has to be compatible with users-modules; E information transfer within modules has to take place using some standards like STEP;

0925-5273/99/$ - see front matter  1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 5 2 7 3 ( 9 8 ) 0 0 1 5 7 - 1

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E the system has to function autonomously as well as in the environment of CAx; E the system has to consist of two levels: decisionmaking (advisory) level and information producing level; E the system has to be based on the principles of AI with opportunities for symbolic equation solving, “optimising”, and information producing; E information exchange has to take place on the basis of unified product model using the part and feature classification system. The system architecture is introduced in Fig. 1. There is a meta-system to develop the system modules. It is a chosen shell or environment for creating expert systems with suitable interface for the field.

The user interacts with the control module consisting of the inference engine and the analyser, the role of which is to work out the search strategy on the basis of initial data given by user, via user’s interface. Configuration of the first level depends on user’s needs and can be reconfigured any time. On this level, decisions will be made and recommendations will be worked out on the basis of minimum data (it means the large DB is not needed). It is meant for human experts working in interactive way with the system without CAx systems. The second level of the system is needed in the environment of CAx systems using consistent databases. Some modules of the proposed system will be introduced.

Fig. 1. Architecture of the advisory system.

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3. Description of the modules 3.1. Turning tool selection module In the proposed approach, the cutting tool is the central part and inheritance of the process components is assumed. Once a suitable cutting tool is found, the other components of the process can be selected (machine tool, fixtures, etc). These components determine the manufacturing process. Also, standard elementary processes can be available in the database. The tool selection task is characterised by the following features: E multivariability of solutions; E large-scale selection criteria; E presence of subjective criteria for the estimation of solutions; E noncorrectivity of cutting process models. The starting point here is the link between the surface and the cutting tool forming it. It means that the manufacturing process is a means of giving the tool an appropriate movement to achieve the required form and quality of the surface. Thus, we can highlight the following rules: E the kind of cutting tool is determined by the kinematics of the cutting process; E the type of cutting tool is determined by the cutting scheme, i.e. how metal is removed; E the construction of the cutting tool is determined by the rules of cutting tool design and the designer’s experience. The model elaborated is based on the following presumptions: E a part consists of the constructional, functional, and technological form features; E there is a finite-dimensioned set of available tools (realizing elements) to machine each form feature; E there are a number of objective functions characterizing the accepted decision. To solve the task, the following initial data is necessary: E a partly sorted set º of the form features (types of holes, slots, etc);

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E a set of realizing elements (RE) K (tooling variants); E conditions, specifying RE as belonging to a certain set. The tool selection task is solved as a sequence of operations: E selection of the kind of tool; E selection of the type of tool; E selection of the construction of tool. The tool selection system does not need a new system, but according to the particular conditions, it can be fitted into the existing one on the virtual level. This module is realized in the environment of Insight 2# and its more detailed description is in [2].

3.2. Tool material selection module In the decision process there always has to be the object on which the decision is made and the subject who makes this decision. So, the decision-making process can be reflected graphically as a decisionmaking tree [3]. Such an approach gives the knowledge engineer, as well as the expert a good overview of what is needed. In this system answers to the following problems are available: E which tool material is suitable for the given part material, tool, and machining conditions; E information on tool material is needed; E the grinding rates for the regrindable tools and inserts are needed; E which synonymous tool material from other producer is available. The module is not closed. New problems related to tool materials can be added. There are several kinds of knowledge in the system. Prior knowledge, such as machining part material kind of tool, machining conditions, tool material the grinding rates looked for etc.

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The kind of prior knowledge depends on the problem to be solved. The prior knowledge can be achieved as input data in real time mode. Decision knowledge (knowledge for the action) E the rules and methods to select the tool material; E the rules and methods to get information on tool material; E the rules and methods to find suitable grinding rates; E the rules and methods to find synonymous tool material. The objectives mentioned above allow different strategies to reach the goal. These objectives are almost related to each other but they focus on different aspects and take different priorities for each objective. By elaborating the MATEXPERT, the expert system shell is used. The module is described in a more detailed way in [3]. 3.3. Part and feature classification module The concept when elaborating the classification system is the following: E a product can be observed as an object with its properties; E the meaning of the part and its properties depends on its exploitation functions; E a product can be split into different levels of abstraction having some engineering meaning (sub-products, parts, form features); E engineering entities will be parts and form features; E the main geometric shapes, used by part design are established. As a result of previous experience the new objectoriented part and feature classification system was introduced [4]. It means that the part will be considered as an entire object with its properties in the context of engineering tasks.

icons and data models to fix information on part in the database. Here, the level means the depth of classification. The first level is divided into rotational and nonrotational parts. The second level is a more detailed description of the main shape: shafts, discs, gears, etc. Unlike other classification systems the form features (FFs) are not taken into account, as manufacturing of the main shape does not depend on FFs related to a given part. This creates a good basis for integration of CAD and CAM. Of course, by using such a kind of classification system for part family formation a great deal of geometrical information can be lost due to the nature of the approach. But, there will not be any problem in linking this optional description to a more detailed description on the feature level, making it possible to use detailed data for CAD/CAM.

3.5. Feature classification system The other problem is feature classification. There is no consensus yet on this field, so, different variations of feature definition exist. When elaborating the system, previous experience is taken into consideration to form the system oriented towards tasks, actual in part and manufacturing design. However, the elementary geometric elements (holes, slots, etc.) are formed taking into consideration such entities as points, lines, and surfaces (feature topology) the entire shape (feature geometry). It is considered that in the stage where a human being is involved in an engineering process, thinking in such terms as holes, slots, etc. is more efficient and easy to survey. When elaborating the system, the following requirements that a feature should at least fulfil, are taken into account [5]: E E E E

it it it it

has to be a physical constituent of a part; ought to be mappable to a generic shape; should have engineering significance; must have predictable properties.

3.4. Part classification system

In order to have a viable system, the following requirements have to be met:

This is a two-level system realized on a personal computer in an interactive way. The system uses

E interactive form feature definition; E geometrical and nongeometrical definition;

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E feature description should be convertible into some standard format; E the system would allow extension and links to other systems. At this stage any difference between the constructive features and manufacturing features is pointed out. Their geometric shape defines both. The main difference is in a data model description. Another peculiarity used in this system is that the FF classification system consists of simple shapes. So, by classifying the combined FFs (stepped holes, etc.) these will be decomposed into simple ones. Relations are described within the data model. The system is realised as classification tree, automatically illustrating the shape of the object (Fig. 2).

3.6. Decision-making supporting module Advisory activities are the decision problem D which is defined as a set of statements about a set of results: D"1X, ½, f 2,

(1)

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where X is a set of choices; ½ is a set of results, and f is a decision function which assigns the result set f : XN½.

(2)

The main problem is to establish this function taking into account constraints. This is a multicriterial task and a suitable method to solve it is needed. In [6] P -space optimisation method is O introduced. A set of constructional, geometrical, and technological parameters a ( j"1, n) performs H the vector A(a ,2, a ), with parametric constraints  L a*)a )a**, (3) H H H which are selected by the way of recommendations, experience or intuition. Constraints (3) forms in n-dimension space the subspace D . V To estimate the perfection of the solution we use local criteria K (a ) (l"1, m), which forms the vecJ H tor of criteria K (K ,2, K ). This vector consists  K of k maximizing and k minimizing criteria. The   argumented definition of boundaries by criteria K (a )*K  ∀l3k , J H J  K (a ))K  ∀l3k , J H J 

Fig. 2. Object-oriented feature classification system.

(4)

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cannot always succeed before the process, and so these have to be specified during the dialog. The subset of the parameters a satisfying the conditions H (3) and (4) will be named further as permissible or alternative set of parameters +a , and the set ? +¸(a ), as a set of permissible solutions. By the way, ? within the permissible set of solutions the set of effective solutions ¸ exists. The solution is effective  when at the points defined by the set +A, there is no other such solution as a where the inequalities  a (a or a 'a are not valid and inequalities     K (a )(K (a ), J  J  (5) K (a )'K (a ), J  J  are valid. One of these conditions has to be strong (' or (). It is important to emphasize that the effective points P cannot be made better simultaneously by I

the criteria K ,2, K . It means that more in K formation on the preferences of the criteria is needed in order to find optimal points. The selection of the testing points takes place by the formula xG "a*#(a**!a*)q (j"1, n), (i"1, N). H H H H GH

(6)

N is a number of testing points and will be defined by the user. The algorithm to calculate the sequence of the points q is given in [7]. GH As a problem, the selection of the evaluation criteria is very actual. There is an opportunity to select one determining criteria or several ones. By the one determining criteria the selection has to be done within the K ,2, K .  K In general case it is rather difficult to do. Here it would be suitable to select complex evaluation criteria (decision coefficient) as function from all the

Fig. 3. A feature based rapid prototype for selecting cutting rates.

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criteria in the form K" a K (A), l"1,2, m, (7) J J where a is a coefficient of relative importance and J the conditions a *0 and a #2#a "1 have J  I to be met. Problems of data modeling related to a given work are introduced in [8]. By elaborating data models it has to be taken into account that: E objects (parts and form features) have the same structure (syntax); E the semantics of the design has to enable the complete description of the object by its functionality; E the semantics of the manufacturing has to enable related process planning in suit tool selection. According to the afore-described concept the part and feature data model structure is elaborated. Practical realization is currently under construction. A rapid prototype illustration is demonstrated in Fig. 3. The part is described as consisting of form features. Activating the form feature in CAM application (the long cylinder in this example) and using the right button in upper icon row, the data transfer to advising block takes place. Evidence of this is the window for add data. As a result, the recommended cutting speed and feed are shown in extra windows. The integrated decision coefficient K is pointed out as well. After this, data will be " transferred to CAM. 4. Discussions The afore-described system development is currently in progress. Realization of this system has

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shown that the problem is real and resolvable, however, there are a number of questions related to contradictory knowledge on the same problem and the ways to define the preference.

Acknowledgements We would like to thank the Estonian Science Foundation for the grant G1755 enabling us to carry out this work.

References [1] P. Gu, D.H. Norrie, Intelligent Manufacturing Planning, Chapman & Hall, London, 1995, p. 337. [2] J. Papstel, Surface-oriented tool set as a new environment for process planning and concurrent engineering, Proceedings of the Estonian Academy of Sciences, Engineering 1 (1995) 75—86. [3] J. Papstel, A. Hein, A. Saks, MATEXPERT — the system for the information, on tool material, 2nd International Workshop on Learning in Intelligent Manufacturing Systems, Budapest, Hungary, 1995, pp. 71—89. [4] J. Papstel, A. Saks, Object oriented part and feature classification system, Proceedings of 7th International DAAAM Symposium, Vienna, Austria, 1996, pp. 319—320. [5] O.W. Salomons, F. van Slooten, H.G. Jonker, Interactive Feature Definition, Advanced CAD/CAM Systems, Chapman & Hall, London, pp. 144—160. [6] J. Papstel, T. Alasoo, Tool selection as a multicriterial optimization task, 2nd International Workshop on Learning in Intelligent Manufacturing Systems, Budapest, Hungary, 1995, pp. 186—204. [7] I. Sobol, R. Statnikov, Vo bor optimalno h parametrov v zadadshah so mnogimi kriteriami, Moskva, 1981. [8] J. Papstel, A. Saks, Data representation problems by realising the advising systems, Proceedings of 7th International DAAAM Symposium 17—19th October 1996, Vienna, Austria, pp. 385—386.