Towards an expert system prototype for composite piezoelectrics design

EISEVIER

PII: SO261-3069(97)00026-S

Materials & Design, Vol. 18, No. 1, pp. l-9, 1997 0 1997 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0261-3069/97 $17.00 + 0.00

Towards an expert system prototype for composite piezoelectrics design

Q. F. Li*sa, H. F. Liua, J. T. Zhanga, W. M. Wanga, Z. Y. Wangb, Q. Liuc

a College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 7 5000 7, People’s Republic of China b Department of Acoustical Engineering, Harbin Engineering University, Harbin 150007, People’s Republic of China ‘Department of Computer and Information Science, Harbin Engineering University, Harbin 15000 1, People’s Republic of China

Received 7 1 December 1996; accepted 21 May 1997

An expert prototype system for composite piezoelectrics design is discussed in this paper. The expert prototype system contains a general inference engine, three material knowledge bases, two meta- knowledge bases and a set of function models. It can simulate a human expert in material design to make a design scheme for composite piezoelectrics, and can be used in engineering applications when the knowledge bases are further perfected. The system has been developed using Borland C++ 3.1 object-oriented programming language. It has an attractive interface and is user friendly. 0 1997 Elsevier Science Ltd

Keywords: expert system; piezoelectrics; design

Introduction Expert systems are rapidly growing in diversity of appli- cation and usage worldwide. They are probably the most practical applications in the field of artificial intelligence (AI). We have moved from the data age to the information age, and are advancing to the knowledge age where AI and expert systems/knowledge-based systems technology will become a major factor. With the advent of modern technology, more sophisticated methodology is needed in design. How- ever, only a few expert systems in material design are available. Composite piezoelectrics which exhibit the desired transduction properties not offered in conven- tional ceramics, are very important in acoustical engineering . l-3 The basic concept for the design of piezoelectric composite materials is to develop multiphase materials by combining the desirable properties of several component phases in a composite through the selection of proper configurational patterns for each phase. Improved properties can be achieved by using,

for example, a ferroelectric ceramic phase and a polymeric phase. Material properties are controlled by the manipulation of a design parameter called the ‘connectivity’, which describes the manner each component phase connects itself in the composite in either zero, one, two or three dimensions. Among which, l-3 piezocomposites contain thin ceramic rods embedded in a polymeric matrix but aligned parallel to the poling direction. By selecting the ceramic and the polymer type, one can control the piezoelectric properties of a l-3 composite which may offer a higher sensitivity, better pressure stability, a higher dielectric constant and lower fabrication cost4v5. Therefore, ceramics and l-3 composites are emphasized in our knowledge-based system. It may be very useful to improve composite piezoelectrics and acoustical engineering.

Although this research is in its infancy, valuable results have already been found which help forecasting promising avenues of research in the development of knowledge-based systems.

EXPERT-KNOWLEDGEENGINEER ---+&N~WLEDGEEDIT~R~

- PERTSYSTEMKNOWLEDGEBASE

Figure 1 The knowledge acquistion approach

* Correspondance to Q. Li

Materials & Design Volume 18 Number 1 1997 1

Prototype for composite piezoelectrics design: Q. Li et al.

System design and function models Model of data input and predisposal

Figure 1 shows the method of knowledge acquisition The model of data input and predisposal consists of while Figure 2 shows the framework of the system three components which are shown in Figure 3. The (note that the inference engine is in the dashed line). BASIC DATA INPUT model is the initial part of the There are several functional models in the system. system on which the user may choose the basic

DATA INPUT AND PREDISPOSAL

G * PIEZOELECTRIC

MATERIAL MANUFACTURE --I

yes USE THE PRESENT MATERIAL

I KNOWLEDGE BASE I

Figure 2 The framework of the system

t I

MATERIAL REFORM DISPOSAL

v

+ J’

MATERIAL COMPOUNDING DISPOSAL ;

I I

----_---------_-_____J

1

SYSTEM DATA BASE

ARRANGEMENT PREDISPOSAL

INQUIRY OF CHARACTERISTIC DATA

( INTO DATA BASE]

Figure 3 The model of data input and predisposal

2 Materials & Design Volume 18 Number 1 1997


OPEN AN APPOINTED DATA FILE

IS THE CHAIN TABLE +

EMPTY ? TAKE A CHARACTERISTIC PARAMETER DATA x 1

FROM THE TABLE CHAIN

IS THE FILE EMPTY ?

I ERROR-

DISPOSAL

1 1 FILE INTO MATERIAL SELECTION QUEUE 1

REPORT ON THE

<IS AT THE END OF PARAMETER TABLE CHAI “diN”D”y;~;AyA;;;;;

VALUES

1 CHECK THE NEXT PARAMETER AND PUT IT AS xl 1

TO THE BEGINNING OF THE

1

_, REPORT ON THE

PRESENT

IS THE QUEUE EMPTY ? Y PARAMETER &

* ITS STANDARD I

Ni-

1 THE CONDITION OF x 1 ?

DELETE THE MATERIAL FROM THE MATERIAL QUEUE

Figure 4 The flow chart of characteristic data inquity

parameters and their value regions. The ARRANGE MENT PREDISPOSAL model can give a proper se- quence of the parameters according to their importance. The INQUIRY OF CHARACIERISTIC DATA model can choose and acquire a proper parameter according to its level of importance. The Flow chart of

INQUIRY OF CHARACTERISTIC DATA is shown in Figure 4.

Model of knowledge editing and testing The method of knowledge acquisition is shown in Fig- ure I.



There are two ways for knowledge-editing in our system:

1. Edit the knowledge base of the system directly. 2. Edit the knowledge base of the system by interac-

tive query.

Knowledge-base testing consists of:

1. Testing of the knowledge-base characteristic. 2. Testing of the knowledge-base sentences structure.

Other function modek Model of explanation. The model of explanation allows the user to question and challenge the results from the expert system as well as to understand how the results are achieved.

Model of arithmetic expression. This expression operation can be done and allows the user to write an operation rule into the program directly.

name: rule3 if

$mponent (PbMglBNb2/303 PbTi03PbZr03) < (Qm, 3880) & then L

add (Mn02) reach (Qm, 3880) endrule

name: rule4 if n L

Component (PbMglBNb2/303 PbTiO3PbZr03) < (Kp, 0.76) & then

2 add (SiOZ) reach (Kp, 0.76) endrule

Figure 5 Two examples of reform knowledge base rule

name: rule1 if

2 Component (KNiO) method (hotpress) & then 1 change (temperature, 1300) endrule

Figure 6 An example of the manufacture condition rule


Model of help. The model of help gives all the illustra- tions and manuals.

Knowledge representation

The most important component of the expert system is the ‘knowledge base’. The knowledge base is the set of facts and heuristic (rules of thumb) about the expert system domain. The power of the expert system lies in its knowledge. Therefore, it is critical that the knowledge base be complete, consistent and accurate6.

In building an expert system, the usual steps used are: problem selection, knowledge acquisition, knowledge representation, knowledge encoding, knowledge testing and evaluation, and implementation and maintenance. The most important one is ‘knowledge representation’. Knowledge approaches include using frames, predicate calculus, semantic networks or

name: rule5 if

Color (grey) then is (loss, Pb) endrule

name: rule6 if 1 is (loss Pb) : 0.9 then

use (atmosphere - piece) : 0.8 endrnle

Figure 7 Two examples of the improvement method rule

name: rule1 if 1 Name (PZTS) then L

choose (polyethylent) = (V, 0.8) endrule

name: rule2 if

Component (CaTiO3) then ? L

choose (stycast) = (V, 0.75) endrule

Fire 8 Two examples of the compounding design rules

production rules 7. In our system, frames are used for the characteristic parameters of piezoelectric materials since the knowledge already exists as descriptions. Rule-based deductions are used for the acquisition of knowledge from design experts.

There are several steps in a material design proce-


steps. To improve efficiency and avoid an enormous search area, the rules are in three different files.

Ch4lng.d

dure and different knowledge is needed at different This is a material reform knowledge base from which a

IS AT THE END OF THE RULE BASE ?

DELETE ALL THE SIGNS OF THE

SIGNS OF THE

THE

1 RECORD THE PRESENT RULE 1

I

SIGN THE PRESENT RULE

1 DELETE ALL THE SIGNED AFFIRMANCE 1

N IS THE DATA BASE SATISFIED FOR THE

OBJECT RESULT ?

Figure 9 The flow chart of the inference procedure


Prototype for composite piezoelectrks design: Q. Li et al.

preselected material can be reformed by changing some elements.

For example, a component (PbMg, 3Nb,,,0, PbTiO,PbZrO,), the Qm is less than 3880, then, MnO, will be added and the Qm value will be reported and inferred until Qm = 3880. The same would apply if the Kp is less than 0.76, then, SiO, will be added until Kp = 0.76. These are shown in Figure 5.

Process.ir1

This is a piezoelectric material manufacture knowledge base for two purposes: (1) to show the manufacturing

START THE MBTA-RULE TESTING IMPLEMENTATION

LOAD UP THE RELEVANT KNOWLEDGE BASE

conditions, such as sintering temperature, heat preser- vation time, for a sintering process (see Figure 6); and (2) to give the improved method according to the obtained materials (see Figure 7).

C0mp.d

A material compounding design knowledge base from which one may obtain the experiential values of material parameters and estimation of material characteris- tics (see Figure 8).

L

1

SINGLE-STAGE INFERENCE

f REPORT ON THE INFERENCE

SUCCESSFUL N

IS IT A F * IMPLEMENTATION

INFERENCE ? I

1 Y

I RENEW THB SYSTEM DATA BASE I

IS IT THE LAST STAGE ?

START THR META-RULE DISPOSAL ? OR RE-INITIATION BY USER ?

I START THE MBTA-RULE DISPOSAL I

I RENEW THE SYSTEM DATA BASE I

Figure 10 The general flow chart of the inference system


Pro to type for composite piezoelectrics design: Q. Li et al.

Figure 11 Opening screen of expert system

Inference engine

The inference engine is the problem processing part of the expert system. It is the control structure of the expert system that allows the expert to use search strategies to test different hypotheses to arrive at expert system conclusions. An approximate inference model of the logical deduction models is used in this system. The flow chart of the inference is shown in Figure 9.

A data-driving control strategy is used in this system. A meta-rule control is also used to improve and aid the user in searching. The inference system is shown in Figure 10.

Organization of the system dynamic data

The dynamic data bases are used to record and store the inference procedure data which include the elements and characteristic parameters of the present material, the presently used rule and the inference procedure fact/affirmance, etc. There are three data- chains, two data bases and two data stacks in the system to organize all the dynamic data.

The storage of the material characteristic parameters

Two table chains are used to store the material characteristic parameters. One is established at the beginning when the materials are selected. It includes all the materials which can be reformed and their characteristic parameters. The other chain is used to store the present material characteristic parameters which

change continuously with the system operation until the final useful data is obtained and recorded.

The record of the present state

Two fact-data bases are used to record the present state. One is the meta-rule-state base which stores the facts that can be activated. It can be renewed by the inference of the meta-rule. The other is the present-inference-rule-state base. The initial state is determined by the meta-inference implementation and will be renewed in different steps of the inference procedure until a satisfactory outcome has been achieved.

The record of the inference procedure state data

Two data stacks are used to record the inference procedure state data. One is an object-stack which records the added and changed parameters. The other is a fact-stack which records the deleted facts.

The record of the used rule

A table chain is used to record the rule used in the operation. It can help the system with backward inference and be used in the explanation of the inference procedure.

Conclusions

1. An expert system prototype of composite piezoelectrics design is established. There are three main



Figure 12 Screen display of the inference process

parts in the system: material reform design, piezoelectric material manufacture design and material compounding design.

2. A general inference engine is complete. It can perform different inferences by loading up different knowledge bases.

3. A system data base has been developed. Data


exchange can easily be done through it to perform every inference step.

4. The system has been developed using Borland C + +3.1 language. The user interface (see Figure II and Figure 12) and compatibility of the system are effective.

5. The system can simulate a human expert of mate-

P rofotype for composite piezoelectrics design: Q. Li et al.

rial design to make a design scheme for composite piezoelectrics, and can be used in engineering applications when the knowledge bases are perfected further.

fessor J.W. Hancock for his helpful discussion during her stay in the Department of Mechanical Engineering, University of Glasgow.

Recommendations for further work

The system currently in use has a number of limita- tions. The system’s knowledge is extremely narrow, it can only perform simple composite piezoelectrics material design. Further work. is therefore needed to improve the capability of the knowledge bases, so that the system can be used in engineering applications. The interface of the system also needs to be improved. Natural language capabilities should be used in the future. As our next major step, the self-learning capability will be developed.

Acknowledgements The author Qingfen Li gratefully acknowledges Pro-

References

Newham, R. E., Skinner, D. P. and Cross, L. E., Connectivity and piezoelectric-pyroelectric composites. Materials Research Bulletin, 1978, 13,52.5-536. Newham, R. E., Bowen, L. J., Klicker, K. A. and Cross, L. E., Composite piezoelectric transducers. Materials in Engineering, 1980,2,93-96. Smith, W. A. and Shaulov, A. A., Composite piezoelectrics: basic research to a practical device. Ferrorelectricity, 1989, 91, 155-162. Smith, W. A. and Aula, B. A., Modelling 1-3 composite piezoelectrics: thickness-mode oscillations. IEEE Trunsactions, 1991, 38,40-47. Smith, W. A., Modelling l-3 composite piezoelectrics: Hydrostatic response. IEEE Transactions, 1993, 40, 41-49. Liebowitz, J., Expert systems: a short introduction. Engineering Fracture Mechanics, 1995, 50, 601-601. Huang, K., Expert System. Southeast University Press, 1988.


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Towards an expert system prototype for composite piezoelectrics design