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 DOI: 10.1177/1063293X9900700103

1999 7: 23Concurrent EngineeringDaizhong Su

Design Automation with the Aids of Multiple Artificial Intelligence Techniques  

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Design Automation with the Aids of MultipleArtificial Intelligence Techniques

Daizhong Su

Department of Mechanical and Manufacturing Engineering, The Nottingham Trent University,Nottingham, NG1 4BU, U.K.

Abstract: In most cases, to design a product through the process of total design is a tedious task. In order to speed up the process andhence to reduce time to market, an intelligent integrated system (IIS) approach has been developed to integrate various stages in total de-sign process, including conceptual design, detail design and manufacture. The integration is achieved by blending multiple artificial intelli-gent (AI) techniques and CAD/CAE/CAM packages into a single environment. The Al techniques utilised in the approach include

knowledge-based systems, artificial neural networks and genetic algorithms. The IIS has been applied in mechanical power transmissionsystem design, which approves that the approach is a powerful tool for design automation. Wthin the IIS environment, the multiple Al tech-niques coexist and complement each other, and thus their drawbacks are eliminated and their advantages are maximised.

Key Words: knowledge-based systems, artificial neural networks, genetic algorithms, artificial intelligence, design integration, engineeringdesign.

1. Introduction

As defined by Pugh [ 1 ], total design &dquo;is the systematic ac-tivity necessary, from the identification of the market/userneed, to the selling of the product to satisfy that need.&dquo; Theprocess of total design consists of several stages, includingproduct design specification, conceptual design, detail de-sign and manufacture. In most cases, to conduct a design~~ and y~a~M/ac~e. In most cases, to conduct a designthroughout the entire process is a tedious and time-

consuming task, due to the variety of complicated activitiesinvolved and extensive expertise required.

In order to reduce production cost and time-to-market, it isnecessary to automate the total design process by means ofcomputer aided integration. This has been attracting great at-tentions from both researchers and industry, with new meth-ods and software continuously emerging. However, chal-

lenges still lay ahead, for instance, some commercial CADtools are costly and extensive development work is requiredfor their application in real world design tasks; many stand-alone software packages are used in industry, but there has notbeen effective means to combine them into an integrated sys-tem ; most knowledge-based systems are not flexible for theuser to alter the knowledge stored in the system; and so on.

In this research, an intelligent integrated system (IIS) ap-proach has been developed to integrate various activities in-volved in the total design process. This is achieved by blend-ing multiple artificial intelligence (AI) techniques and

CAD/CAE/CAM packages into a single environment. TheAI techniques concerned in this research include knowledgebased systems (KBS), artificial neural networks (ANNs) andgenetic algorithms (GA).

Each AI technique has its advantages and drawbacks. Theapproach provides an integration environment where the AItechniques complement each other, and thus the drawbacksare eliminated while the advantages are maximised.

In following sections, the overview of the approach is pre-sented first, including the software integration, an IIS forpower transmission system design and the AI complemen-tary features; and then three important aspects of the IIS arefurther described: KBS-ANN combination for conceptualdesign, multiple AI complementation for detail design andGA optimisation of ANN architecture.

2. An Overview

2.1 The IIS Approach

As shown in Figure 1, there are two types of integrationwithin the IIS approach: the integration of all the majorstages of total design, including product design specification,conceptual design, detail design and manufacture; and the in-tegration of artificial intelligence techniques with variousCAD/CAE/CAM packages. The former is achieved by utilis-ing the latter. The software techniques and packages/pro-grams involved in the IIS and their relationship are shown inFigure 2.

The design expertise is captured by the KBS and ANNs,GA is employed for optimisation and hypermedia is used toprovide effective means for user interfaces and data transfer.Other tasks within the total design process, such as numerical

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_

Figure 1. The IIS approach. _

analysis, engineering drawing and data processing, are con-ducted using relevant CAD/CAE/CAM software packages.The KBS communicates with the others and works as a coor-dinator controlling the design.

2.2 IIS for Power Transmission System Design

As an application of the approach, an IIS for total design ofpower transmission systems has been developed. The overalldesign process is controlled by a master KBS developed usingC++, in conjunction with a friendly graphical user interface(GUI) developed using Visual BASIC. Its functions are

briefly described below; for further details, see Reference [3].

2.2.1 FORMULATION OF PRODUCT DESIGNSPECIFICATION (PDS)The PDS items are specified by the user via the GUI. The

PDS are two types: initial requirements such as transmission

Figure 2. Software integration within the IIS.

power, orientation of input/output shafts, speed ratio, centredistance, etc.; and evaluation criteria including size, manu-facture cost, easy manufacture, etc. The system has a mecha-nism for further development to add more PDS items. TheGUI also interfaces with the databases to store and retrievethe specified PDS.

2.2.2 CONCEPTUAL DESIGNThe concepts to be constructed by the IIS fall in the fol-

lowing range:~ stages of the transmission: one, two or three~ orientation of input/output shafts: parallel, cross and per-

pendicular~ components at each stage of the transmission: seven types

of components including gears, belts and chains

Examples of the concepts are shown in Figure 3. In this phaseof the design, four ANNs are used to generate the concepts,which will be further described in Section 3. Based on the

output results from the ANNs, the KBS makes a decision toselect the best concept for detail design.

Figure 3. Examples of concepts.

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2.2.3 DETAIL DESIGNThe selected concept is decomposed into sub-systems.

The detail design of each sub-system is conducted first, andthen the completed sub-systems are assembled together toform a final design. At this stage, conflicts may occur and theKBS has the capabilities to resolve the conflicts by redesign.Currently, the IIS can handle the detail design of two types ofsub systems: gearboxes and belt drives. The final system canbe one of them, or combination of both. The tasks conductedin detail design include: gear strength analysis, bearing selec-tion, shaft design, case design, belt and pulley selection, de-sign optimisation, component and assembly drawings, andparametric design of components.

In this phase of the IIS, in addition to the KBS, GUI anddatabases, the following software programs/packages are in-volved :

~ C++ programs and a commercial package FennerBelt. Theformer are used to conduct gear strength calculations andshaft design, while the latter is used for belt and pulley se-lection.

~ ANNs developed using C++ for two applications: to con-vert design graphs for detail design, and to retrieve exist-ing design from component design data bases.

~ A GA program for searching the best combination of de-sign parameters used in the gearbox design.

~ Pro/Engineer is integrated into the IIS to produce compo-nent and assembly drawings, using parametric designmethod and to produce CNC programs for the designedcomponents.

2.2.4 MANUFACTUREIn this phase, the IIS can produce cost analysis of the de-

signed system, process planning for manufacture of majorcomponents, and CNC programs for manufacture of shafts.

2.3 Complementary Features of the Multiple AIApplied within the IIS Approach

Recently, artificial intelligence techniques have been

widely applied in engineering design, and have been seen aspowerful tools to enhance design qualities. However, in ad-dition to their advantages, each AI technique has its own

drawbacks which limit their application. Those can be sum-marised as follows:

~ The major function of knowledge based systems is sym-bolic reasoning, which is particularly useful to capture de-sign expertise in the category of decision-making. How-ever, the design knowledge must be clearly expressed inthe format required by the development tool, and the sys-tem cannot adapt to change after implementation.

~ Since ANNs are example-based, their knowledge can bealtered by training the networks using new training data;and during the training, the cause of the mapping inputdata to output pattern is not required, which overcomes theknowledge acquisition bottlenecks that design experts can

tell what the results are but may have difficulties explain-ing why. However, the training may be time-consuming,the input/output data has to be interpreted, and the deter-mination of ANN architecture is a difficult task.

~ Genetic algorithm (GA) is an adaptive search techniquewith advantages over traditional search methods such ashill climbing and the Newton-Raphson methods. It is par-ticularly suitable for optimisation of multiple parametersto obtain a global optimum. However, GA’s successfulutilisation depends on the experience in determination ofthe algorithm parameters, and the pre and post processingis required for encoding and decoding.

~ In product design, there are two types of knowledge: pro-cess knowledge and activity knowledge. The former is re-garding the decision-making for design process control,such as the sequence of conducting tasks and connectionbetween the design stages. The latter is related to conduct-ing a particular task, such as concept generation and pro-ducing a component drawing. The ANNs and GA are suit-able to handling the latter but not the former. In contrast,knowledge-based systems are capable of handling theformer but may be not suitable for the latter.

This approach combines multiple AI techniques into a sin-gle environment, where they coexist and complement eachother.

Within this approach, the KBS is used to deal with theknowledge which can be clearly defined and expressed,while the ill-defined knowledge is handled using ANNs; theANN architecture parameters, such as number of layers andneurons in each layer, are optimised using GA; the expertisefor determining GA’s algorithm parameters, such as crossover rate and number of generations, are handled by ANNs sand KBS; the tasks for data interpretation and pre/post proc-essing for ANNs and GA are conducted using KBS. The pro-cess knowledge is handled by KBS, while GA and ANNsdeal with activity knowledge.

Therefore, the integration of the AI techniques into the IISprovides an effective means to eliminate their drawbacks andmaximises their advantages. This is to be illustrated in thefollowing sections.

3. KBS-ANNs Combination for

Conceptual Design

Within the IIS for power transmission system design, thebasic idea to create a concept is to assemble component(s)into the positions of an arrangement pattern. As shown inFigure 4, the concept of a two stage gearbox shown in Figure4(c) is formed by assembling two components, helical gearand spur gear shown in Figure 4(a), into the positions 1 and 2of the arrangement pattern shown in Figure 4(b) respectively.

The scope of both the arrangement patterns and compo-nents have been mentioned in Section 2.2.2.

In order to describe the approach explicitly, those shownin Figure 4, three components (spur gear, helical gear and

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, Figure 4. Concept construction.

double gear) and one arrangement pattern (two stages withparallel orientation of input/output shafts), are used as exam-ples in the following description.A method combining KBS and ANNs has been developed

to construct a concept. The KBS controls the design processand works as a pre/post processor for the ANNs. Four ANNsare used, one for selecting an arrangement pattern and theother three for component selection.

3.1 The KBS Controlled ConceptualDesign Process

The concept generation process is shown in Figure 5. Theprocess is controlled by a process controller, which is a KBS.The KBS also communicates with the IIS system controllerto ensure that the work is conducted within the IIS’ global en-vironment.

According to the design specifications, the KBS determinethe number of stages first using production rules, examplesof which are shown below.

IF overall transmission ratio <4THEN the concept is of one stage with parallel shaft ori-

entation ,

IF overall transmission ratio > 4 < 7

THEN the concept is of either one stage or two stage with

parallel orientationThe KBS then calls the arrangement ANN to select a suit-

able arrangement pattern.The next step is to select components for each position of

the determined arrangement pattern.According to the domain knowledge of gear design, for the

design of a gearbox based on the situations shown in Figures4 (a) and (b), the following principles are applicable and therelated ANNs have been developed accordingly:· If a double helical gear pair has been chosen for position 1,

then all the three types of gears can be taken for considera-

Figure 5. Concept generation process.

tion for position 2. The all component ANN is specially de-veloped for this case.

~ If a helical gear pair has been chosen for position 1, then adouble helical gear pair cannot be taken for considerationfor position 2, but the other two types of gears can. The nodouble ANN is specially developed for this case.

~ If a spur gear pair has been chosen for position 1, then onlya spur gear pair can be considered for position 2 while theother two types of gears cannot. The no helical ANN is

specially developed for this case.

Having coded the above design knowledge into the knowl-edge base, the KBS invokes individual component ANN toobtain a component. The following are the production rulesfor invoking a suitable component ANN.

IF select component for stage 1 ’

.

THEN call all component ANN

IF gear pair at stage 1 is a double helical gear pairTHEN call all component ANN

IF gear pair at stage 1 is a spur gear pairTHEN call no helical ANN

IF gear pair at stage 1 is a helical gear pairTHEN call no double ANN

The results obtained from the ANNs are numerical data,and the KBS interprets the data into the forms understand-able to the user.

3.2 The ANNs

Both the arrangement ANN and the component ANNs areof back-propagation and feed-forward networks with sig-moid mathematical function. The ANNs were developed us-ing C++. The software has been designed to give flexibility

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Figure 6. Artificial neural network.

Figure 7. Flow chart of gear design process.

Figure 8. Examples of gear structure.

to its user to determine the architecture of a network in termsof number of layers, number of processing elements (PE) ineach layer, and connections between the PEs. A friendly userinterface is also provided to guide the user to do so.

The input to the ANNs are design specifications, and theoutput from the ANNs are concept arrangement patterns or

components, as shown in Figure 6.The input values are binary numbers with 1 meaning that

the specification item is required while 0 meaning it is not.The output values are real numbers within the range from 0 to

1, and the winning concept has the largest number.Before the ANNs can be used for a design task, they have to

be trained. The training data consists of two parts: the inputdata to the network and output data which are the known re-sults corresponding to the input data. The neurones are con-nected via weights that are adapted during the training. Up onthe completion of the training, the weights are saved into files.The design knowledge is stored in the saved weights.

4. Multiple Al Complementation for Detail Design

Due to the limitation on the length of the paper, the geardesign, which is part of the IIS for power transmission sys-tem design, is taken as a vehicle to illustrate the complemen-tation of multiple AI for detail design. The design process isshown in Figure 7. The overall process is controlled by theknowledge-based system using production rules as shownbelow.

IF Goal is gear design ’ ’0,.,. &dquo;

THEN execute Factors module

AND execute Initial Design module

AND execute Optimisation module

AND execute Conflict Check module

IF no conflict detected

THEN go to the next design stage

ELSE execute Redesign module

AND Goal is gear design

In addition to the KBS, other two AI techniques, ANNsand GA, are also involved within the gear design process.The ANNs are used in two places: obtaining design factorsand design retrieval, i.e, steps 2 and 5 in Figure 7. They areback propagation feed forward networks. The GA is usedfor optimisation of gear design parameters, i.e., step 4 inFigure 7.

4.1 ANNs for Obtaining Design Factors

Within the gear sizing process, the values of safety factors,pitch accuracy, lead accuracy and load factor KH¡3have to bedetermined. Those values are initially provided by design

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graphs, which are for use in a manual design process, andnormally the original data that the graphs were constructedfrom are unobtainable, which makes it difficult to includethem into a computer integrated program. However, the ap-plication of ANNs can solve this problem. In this circum-stance, four ANNs, one for each the four factors mentionedabove, have been trained using the data obtained directlyfrom the graphs, which provides a desirable solution to en-code the design process into the system. For further details,see References [2] and [5].

4.2 ANNs for Design Retrieval

There are various options for gear structural design, someof which are shown in Figure 8. To choose a proper type ofthe structure for gear design requires design expertise, andthe selection principles vary for different applications.Therefore, the knowledge stored in the IIS may need to be al-tered or updated. Traditional knowledge-based systems orexpert systems cannot meet this requirement, because al-though they can record the design expertise into their knowl-edge bases, they cannot adopt the change of the knowledgestored in the knowledge bases.As the means of encoding the knowledge into an ANN is to

train the ANN using training data, the knowledge stored inANNs can be altered by training the ANN using new sets oftraining data. An ANN has been developed for this case. Figure9 shows its procedure for the knowledge recording process.The user, who is expected to be an experienced gear de-

signer, provides the training sets for the ANN via the GUI.The input sheet provided by the GUI consists of two parts:(1) a list of product design specification items such as trans-mission power, cost, size, etc., which affect the gear structuredesign, and (2) a list of types of gear structure designs simi-lar to those shown in Figure 8. A training-data set is producedin the following way: the user specifies a set of specificationsby highlighting some items in the specification list, and alsoselects one gear structure design from the structural designlist, corresponding to the specified design specifications; andthen the selected information is saved into the database. Byrepeating the procedure, a number of training sets can be ob-tained. The design specifications are the input to the ANNand the selected design structure is the winning output of theANN. The function of the pre-processor is to convert the ob-tained specifications/structure sets into a form suitable forthe network. Using the training data obtained, the training ofthe ANN is conducted using the GA training method de-scribed in Section 5.

The input layer of the ANN has the same number of neu-rones as the number of specification items listed in the GUIand a input neurone represents a design specification item;similarly, the output layer of the ANN has the same numberof neurones as that of the structural designs listed in the GUI,and one neurone represents a corresponding structural de-sign. The values of the output data are real numbers, and theselected design has the highest value. The ANN structure, in

Figure 9. Training of design retrieval ANN.

terms of number of hidden layers, number of neurones in thehidden layers, weights, etc, are determined by the GA train-ing method.

After the training, the ANNs can be directly used by theIIS. Once the user inputs the design specifications, the IIScan retrieve a proper gear design structure.From the process described above, it can be seen that the

ANN method provides a flexibility to alter the design knowl-edge stored in the system without changing the system’s pro-gram source codes. Moreover, this can be done by the userwithout the involvement of the system developer. Obviously,this is an important achievement in knowledge engineering.

4.3 GA for Design Optimisation

Optimisation of gear design is a complicated task, andconventional search techniques, such as hill climbing and theNewton-Raphson method, would have difficulty in achiev-ing a global optimum. Therefore, the adaptive search tech-nique of a Genetic Algorithm (GA) has been applied in thisresearch. The GA’s objective functions considered include:

. Achieve the minimum face width and module while com-

plying with BS 436 part 3-not exceeding the permissiblebending and contact stress on the teeth.

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~ Bending stresses within both the pinion and wheel will beapproximately equal.

~ Contact ratio is to be maximised in order to reduce vibra-tion and noise.

_ ,

. Speed ratio is to be maintained. ’

. Centre distance of gear pair to be minimised for variableand maintained for fixed center distance.

Nine design parameters have been considered as the opti-misation variables, including:

. tooth width

. module

. pressure angle

. helix angle

. number of teeth in the pinion

. number of teeth in the wheel in the case that the centre dis-tance can be altered

. addendum coefficient for tooth length modification· addendum modification coefficients (profiles shifts)· rack tip radius ,

For further detail, see Reference [4].As demonstrated above, this gear design example reveals that

within the integration environment of the IIS, each AI techniqueperforms the tasks for which it is best suited and complementeach other, forming a global powerful tool for the design.

5. Genetic Algorithms for Optimisation ofArchitecture of the ANNs z

As described in the previous sections, there are severalANNs in the IIS and all of them are back propagation net-works. Training of this type of network has proved to be adifficult process, due to the lack of effective rules and guides.To overcome this problem, a GA has been developed.

Based upon the performance (fitness) of the network, theGA is used to optimise the topology, transfer function andtraining period, which are the major factors affecting net-work performance. Optimisation of these factors is per-formed simultaneously, with consideration of their com-bined effects upon performance and convergence. This

obviously makes the optimisation more effective.The information relating to the factors that affect the per-

formance of a network during training are encoded into thegenes in binary form. Figure 10 shows an example, whichcorresponds to a sigmoid transfer function, 14 elements inthe first hidden layer and 8 elements in the second hiddenlayer and a training factor of 5.

Figure 10. Genes within a chromosome.

Figure 11. 8A optimisation of ANN architecture.

The optimisation process is shown in Figure 11. Upon ini-tiation, the values contained within the genes are randomlyset from values within the search space. The networks corre-

sponding to the information contained within all the chromo-somes of the population are determined and sorted into orderof descending fitness.

The fitness value of the chromosome is determined by thetest results from the test cases when applied to the trainednetwork. The lower the average error between the desiredand network outputs the fitter the chromosomes. As the pro-portion of the roulette wheel that each chromosome occupiesis allocated, dependant upon its fitness; the fitness value istaken as the reciprocal of the error. Therefore, it is ensuredfor the fitter chromosomes to get the larger proportion of thewheel and the greater probability of reproduction.

Chromosomes are reproduced to form the majority of thenext generation. The reproduction accounts for approxi-mately 98% of the next generation. The remainders are ran-dom chromosomes unaffected that pass through to the nextgeneration. Once the next generation is established, there is aprobability of 2% of introducing an element of mutation intothe population helping to prevent localised minimum beingobtained. The probability of mutation is set low to alloweventual convergence upon an optimum. The next generationis now complete and ready for the new levels of fitness to bedetermined. After the final generation, the network repre-

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sented by the fittest chromosome will become the final net-work ready for application and all the connective weights re-corded.

6. Conclusions

An intelligent integrated system approach has been devel-oped to integrate various stages of total design, includingconceptual design, detail design and manufacture. The inte-gration is achieved by blending multiple artificial intelligenttechniques and CAD/CAE/CAM packages into a single envi-ronment. It has been applied for the design of power trans-mission systems.

The AI techniques concerned are knowledge-based sys-tems, artificial neural networks and genetic algorithms. Withinthe integration environment of the IIS, each AI technique per-forms the tasks for which it is best suited and complementeach other, forming a powerful global tool for design.As one of important features of this approach, it provides

an ANN method for knowledge recording. It has flexibility toalter the design knowledge stored in the system without

changing the system’s program source codes. Moreover, thiscan be done by the user without the involvement of the sys-tem developer. Obviously, this is an important achievementin knowledge engineering.

Acknowledgements

The author thanks Mr. M. Wakelam, PhD candidate atTNTU, Mr. A. Cooper, MEng student at TNTU, and Dr. X.Wang, Visiting Research Fellow from Harbin Institute ofTechnology, China, for their contribution to this research.

, , References

1. S. Pugh, 1990, Total Design: Integrated Methods for SuccessfulProduct Engineering, Addison-Wesley Publishing Company.

2. D. Su, 1998, "Application of Expert System and Artificial Neu-ral Networks for Design Knowledge Retrieval," Proceedings,Engineering Design Conference ’98, an International Confer-ence at Brunel University, Brunel, UK, 23-25 June, pp 371-378.

3. D. Su and M. Wakelam, 1997, "Intelligent Integrated System forthe Design of Power Transmission Systems," Proceedings, TheInternational Conference on Mechanical Transmissions andMechanisms (MTM’97), Tianjin, China, 1-4 July, pp 1010-1014.

4. D. Su, M. Wakelame, K. S. Henthorn and K. Jambunathan, "ASoftware Package for Evolutionary Optimisation of Gear De-sign," Evolutionary Design by Computers, CD-ROM, P. Bentley(ed), Academic Press, (in press).

5. K. Jumbunathan, M. Wakelam, K. Henthorn and D. Su, 1996,"Integration of Multimedia, Artificial Neural Networks and RuleBased Systems for Gear Design," Proceedings, The Interna-tional Conference on the Theory and Practice of Gearing, 4-6December, Izhevsk, Russia, pp 453-468.

D. Su ’

Dr. D. Su is a Reader and the

Chairman of Engineering Design &CAE in the Mechanical and Manu-

facturing Engineering Departmentof the Nottingham Trent University,U.K. He leads the Concurrent Engi-neering Research Laboratory. Hisresearch interests include CAD/

CAM/CAE, AI for engineering de-sign, Internet based design & manu-

facture and evolutionary optimisa-tion with more than 90 refereed

publications. He holds a membership as an academician ofthe International Information Academy, United Nations; andhas been involved in organising nine international confer-ences as Chairman, Vice-chairman or member of interna-tional and/or programme committees.

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