casting. Gravity or sand casting involves pouring the ... · of aluminium over other metals is that...

Alexsys - An expert system for the

aluminium pressure die casting industry

C.A.G. Webster & M. Weller

School of Science and Technology, University of

Teesside, Middlesbrough, Cleveland TS1 3BA, UK

ABSTRACT

ALEXSYS, a prototype expert system was designed for use in the

aluminium high pressure die-casting industry. The aim of the system was

to reduce wastage within the industry, which accounts for a considerable

sum annually. The system was divided into two separate components, the

Adviser and the Predictor system, one to offer advice regarding dies

already in production and the other to offer optimum values for dies in the

design stage. Two knowledge sources were used, one a research

consultancy and the other a foundry. The system was designed to reflect

the sub-sets of knowledge within the foundry. A small scale validation has

been performed.

INTRODUCTION

Casting of aluminium is a major industrial activity that in Europe has an

estimated annual turnover of approximately 3.25 billion ECUs. Typical

markets for Aluminium die castings are the automotive industry and

consumer durables. With an emphasis in industry now on recycling

aluminium is seen as a viable alternative for many parts. The advantage

Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517

206 Artificial Intelligence in Engineering

of aluminium over other metals is that it is strong and light, has excellent

corrosive resistance, conducts electricity well and has excellent fabricating

properties.

The two main methods for casting aluminium are gravity and pressure

casting. Gravity or sand casting involves pouring the molten alloy into a

closed mould that is then allowed to cool, before knocking out the casting.

The process involves relatively inexpensive equipment and due to the

laminar fill, produces castings that are almost free from entrapped air. It

is a slow process however, and this long filling time means that thin-walled

sections may freeze before the casting has been completely filled. This

places a limitation on the type of casting that can be produced.

In pressure die-casting expensive presses are required that have locking

forces ranging from 150 - 2000 tonnes or more. The process of high

pressure die casting involves injecting molten Aluminium into a cavity at

high velocities and under great pressure. There are three stages in the

injection process:

First Stage- a slow injection phase, whereby the melt is pushed along

the shot sleeve by the piston to the position where the gate enters the die.

Typical piston velocities are 0.3 m/s.

Second Stage - A fast stage where the majority of the cavity is filled.

Typical piston velocities are 4 m/s, with the actual melt reaching velocities

of 30-100 m/s as it passes through the narrow gate into the die cavity.

Third Stage - This is an intensification phase where the piston exerts

the maximum pressure, of 400 bar plus, hence the strong locking forces

required of the machines. There is little actual movement of the piston in

this phase, its purpose being to compress any entrapped air, compensate

for shrinkage as the melt cools, and to ensure all areas of the cavity are

filled.

Cooling channels are used to keep the temperature of the die at a

constant level. This is necessary due to the aggressive nature of

Aluminium. After a predetermined interval the die opens and the casting


Artificial Intelligence in Engineering 207

is ejected by pins. The die is then sprayed with lubricant, more melt is

ladled into the shot sleeve and the process begins again. A typical cycle

will take 30 seconds.

The pressure and velocities involved result in a very rapid filling

process, with fill times being around 40 ms. This enables the casting of

much finer parts than by gravity, and with a higher dimensional accuracy.

A permanent mould and the quick cycle time means that the parts can be

mass produced.

Problems arise with such a rapid process, and with such high velocities,

temperatures and pressures involved, small variations can have major

consequences on the quality of the casting.

The main two product characteristics of a casting are good surface

appearance and structural strength. There is a trade-off between these

two with regards to time taken to fill the cavity. A long fill time will give a

high density as all the air can be expelled, but will result in a bad finish

because the melt has time to cool and will exhibit cold flows where hot

and cold sections meet. A short fill time will give a good finish but will

usually contain entrapped air. Besides this fundamental conflict there are

additional problems such as oxide formation, entrapment of vaporised

lubricant, shrinking, etc all of which are inter-related.

With wastage in the pressure die casting industry in Europe currently

running at 260 million ECUs per annum, even a small improvement in

product quality could result in large savings. Alexsys was therefore

conceived as an expert system to aid the reduction of waste in a pressure

die casting foundry.

In constructing the system the aim was not only to aid the foundry in

the identification of defects and to offer advice as to how these might be

eliminated, but to offer a better understanding of the process to many of

those involved. The correction of defects is still viewed as something of a

"black art" in many foundries, with one or two experienced members of

staff who will effect a cure by slight alterations to the die press.



KNOWLEDGE ELICITATION

Two main sources were located to provide the knowledge. The first being

a consultancy in Germany that had conducted extensive research into die

casting. This had been performed with rectangular billets and various

commercial dies linked to a data collection system, that recorded such

parameters as piston velocities, die temperature, pressure in the cavity,

etc. The second source was a foundry in Greece that supplied parts to

well known companies in the consumer durables, computing and

automotive industries.

The nature of the two institutions meant that they could provide

different types of information. The academic partner could provide vast

amounts of data, for instance component density measurements

corresponding to numerous parameter settings. They could effectively

induce bad conditions to examine the efficacy of various remedies. They

also had a large collection of previously cast billets with appropriate data

that could be examined.

With the foundry, financial considerations meant they could not afford

to spend time performing such investigations, but they did possess a wider

range of samples and a greater interest in the economic aspects of

producing quality products. The flow dynamics and thermal patterns

involved meant that what may be an effective remedy for a flaw in a

rectangular billet is not necessarily so with a complex geometrical design,

where there are many changes in flow direction, altering thicknesses, etc.

They also possessed knowledge regarding previous defects and how these

had been overcome. Also they were more aware of competitive market

considerations, which would have to be incorporated into any system to

satisfy end-users of its viability.

It was decided to commence the knowledge elicitation with the research

partner as it would be experienced in relating information regarding the

process to novices. Geographical constraints meant that the expert could

not be consulted with the regularity that might have been desirable. To

this end the use of documentation provided by the expert (Mertz *),

provided a valuable foundation for the actual elicitation sessions. The



advantage of using a report as the basis for further elicitation is that it

saves time in familiarising the knowledge engineer with the domain and

makes the expert comfortable with the topics to be discussed. A possible

disadvantage is that although additional information is gained the

knowledge domain is constrained at the start. It may be that the

knowledge engineer needs to elicit knowledge from the expert in a

manner in which they have not previously expressed it, or to make them

aware of tacit knowledge. To this end several formal knowledge

elicitation techniques were adopted such as Teachback (Johnson &

Johnson %) and conceptual sorting. Conceptual sorting proved to be

particularly useful in grouping defects and causes (see Fig 1). These were

effective in eliciting intermediate steps in the explanations, that the expert

viewed as implicit in the conclusion, but would not appear so to a novice.

Later prototypes of the system were used as a basis for further

development. Six periods of elicitation were conducted ranging in time

from a day to four weeks. In addition to these three joint sessions with the

foundry were also held.

After a reasonable familiarity with the subject had been gained the

foundry was consulted. The first task was to identify exactly whose

expertise the system should incorporate. With the previous partner this

had not been such a problem as the expert possessed at least a theoretical

expertise of all aspects of the process, having supervised and conducted

experiments into most areas. Within the foundry however, the existence

of several experts became a factor. Firstly the machine operator, who is in

the front line, will have gained some knowledge through repeated

experience. This represents a very pragmatic aspect of the knowledge

base. Suggestions that the good machine operator regards as time

consuming or impractical will not be met with great enthusiasm. Secondly

there is quality control. This aspect is mainly concerned with locating and

diagnosing defects. As such it forms a sub-section of the knowledge base

that identifies the defect present in the casting. Perhaps the most

important expertise would typically be that of the production manager,

who oversees all these aspects and will select any alterations to the

machine, and it was this expert who contributed most to the final system.



to



The techniques described above for knowledge elicitation were

attempted in the foundry but they were not met with enthusiasm. They

were either deemed artificial and detached, and thus inapplicable in a

"real world" situation or that they interfered with the flow of an

explanation. Therefore a more unobtrusive method of elicitation was

adopted, based on protocol analysis followed by interview. This was

achieved by observation over a period of days. This allowed the

knowledge engineer to follow parts through the process. With the

knowledge already acquired from the academic partner informed

questioning was possible so as to gain a substantial amount of knowledge

to supplement that already possessed.

Some conflict arose between experts relating to certain advice and

terminology. This meant that multiple expert sessions were necessary to

locate the source of the conflict. Three main techniques for such sessions

have been suggested (McGraw and Searle 3); brain-storming, consensus

decision making, and nominal group technique. As the intention was to

consolidate existing knowledge consensus decision making was the

approach adopted. In combining the opinions of different experts various

formal methods were examined and tested (eg. Lyng & Rudd *). They

were found to be unpopular with the experts and somewhat cumbersome.

For instance it is necessary to rate the dependence of the different experts'

knowledge when using the method given above, according to whether they

arise from the same source. This can be difficult, for example most die-

casters will be acquainted with a standard classification of defects

(Walkington and Bruner 5). To what extent can this be viewed as

rendering their viewpoints dependant? The adopted approach was to use

a rating scale for defects and causes as the basis for eliciting justifications

when differences arose. This demonstrated that most differences occurred

as a consequence of viewing a defect as the product of different

fundamental phenomena, such as a thermal defect or a flow dynamics

problem. By emphasising that the solutions should be viewed within the

framework of the system and therefore the most convenient should be

ranked first some consensus could be reached.



DEVELOPMENT OF THE PROTOTYPE

In reducing waste in the industry it is important to note that the majority

of wastage occurs with a new die. This continues until optimum

parameters are determined. It was therefore deemed necessary to

construct two systems. The first acts as a first-aid kit, that is when a defect

is found in the casting that renders it unacceptable to the customer then

the expert system is consulted to diagnose the defect and to offer advice as

to how to eliminate it as quickly and cost efficiently as possible. This was

known as the Adviser System. The second system offers guidance on

initial set-up of a machine before casting has begun, and this was termed

the Predictor System. This includes recommendations on the design of the

runner and gating system, so that expensive alterations later will not be

necessary, and also reduces the time taken to reach optimum conditions,

with regards to specific product requirements, such as surface finish.

Adviser System

During the course of the knowledge elicitation several, almost

independent, knowledge sub-sets were identified. These were often

performed by different people during the process, or at least represented

separate tasks for the expert. The sub-sets were:

1) Diagnosis of defect

2) Affecting corrective action to alleviate above.

3) Preparing or altering die designs, runner layouts, etc.

It was decided that an effective and useful system should mimic this

separation of tasks, and to this end a modular approach was adopted for

the Adviser system (see Fig. 2).

The diagnostic module took the form of a pictorial gallery through

which the user was guided by making various selections, such as site of the

defect, method of detection, etc. A pictorial representation was decided

upon as it was felt that a textual description would fail to convey subtle

differences between defects. The initial correct diagnosis of the defect is

obviously of prime importance as not only will the corrective advice fail to



reduce it, but in some cases it may aggravate the actual defect. Another

important advantage in pictorial representation is that it overcomes not

only language barriers within Europe, but also those within the industry.

Each defect may have three or more popular names and no formal

classification exists, so this represented another area for possible

confusion. Once the user has selected one or more images this

information is passed to one of the corrective modules, where it is

interpreted as the appropriate defect.

User selectsdefect

Adviser System

Userknowsdefectalready

User wantsquick, cheapsolution

No solutionadequate

Figure 2 - Structure of Adviser System

The first corrective module is that of machine parameters. This would

offer such solutions as altering the piston velocity, cooling flow rate,

available pressure, etc. These are generally easy to alter and they would

be the solutions attempted first in a foundry, because they are the least

expensive. The solutions were ranked for each defect by taking into



account their causal influence on the defect, and the ease with which they

can be altered. Thus ideally the user would work down the list, as these

represent the most efficient means of eliminating the defect.

The second corrective module is that of design. This includes such

considerations as the shape and position of the gate, the runner layout,

cooling channel layout and so on. These factors can have a very profound

effect on the quality of a casting. If the gate is poorly positioned such that

the melt has to travel too far to fill the cavity, then no amount of adjusting

the process parameters will eliminate the defect. However due to the cost

of altering a die, and the lost production involved, foundries are reluctant

to resort to such measures. There is also a middle ground, which can be

termed "start-up parameters." This includes factors such as machine

selection, shot sleeve length and diameter, type of die lubricant, furnace

maintenance, etc. They are parameters that will cause a stoppage in

production but their financial implications are not as severe as altering die

design. They were included in the design module as the degree of overlap

with this knowledge sub-set was deemed greater than with the machine

parameters module. The nature of the advice offered in this module was

more qualitative compared with the quantitative advice in the previous

module.

Predictor System

With the prototype of the Predictor System the modular construction

utilised in the Adviser System was not deemed suitable. In calculating

optimum parameters there is a far greater degree of interdependence

between the areas of knowledge than existed in the previous system. For

instance, a given product characteristic will determine the desired gate

velocity and cavity fill time, which in turn are used in calculating the gate

area. Thus most parameters are influenced by a wider set of variables

than with the Adviser system, where certain elements were taken as fixed,

or at least resistant to change. This offers a greater element of flexibility

and to accommodate this a range of optimum values over the prime

parameters was given, to allow the user to select those most appropriate to

their working practice.



VALIDATION

The systems were evolved using prototypes as a means for further

development, therefore certain aspects were validated as they progressed.

A preliminary validation of the completed prototypes was performed over

a week long period at the foundry. This was achieved mainly by

observation of two operators using the system, interviewing and

comparison against a test run of two separate samples. The main points of

interest for each module were:

1) The diagnostic system was deemed a useful tool, and some

suggestions for its enhancement were gathered, such as checks against

defects of a similar appearance.

2) The machine parameters module performed well on the two

samples, substantially alleviating cold flows in one and porosity and oxide

patches in the other, over a series of thirty samples.

3) The design module offered some useful advice. It was often felt

however to be too general in nature. A greater degree of knowledge

regarding the specific design was required.

4) Although, as indicated, the modular approach was not thought

feasible for the Predictor system, comments revealed that it would have

been preferred. The greater element of flexibility was often found

confusing. A step by step "hardening" of options was suggested.

Sell has suggested five elements to validation, namely; consistency,

completeness, soundness, usability and precision (Sell 6). In this short

validation the emphasis was on the aspects of soundness and usability. A

longer, more rigourous validation incorporating ratings for user

satisfaction and questionnaires would be required to fully address all

aspects of the system's validity. Also a wider user base would be necessary

to overcome any idiosyncrasies of practice.



CONCLUSION

In developing the system the importance of cooperation with the experts

has been emphasized repeatedly. This has involved a good deal of

flexibility on the part of the knowledge engineer, such as altering

knowledge acquisition methods to suit the demands of the expert.

Similarly it was felt the structuring of the system should emphasise the

nature of the knowledge gained. This was not intended to make the

system a model of the expert's cognitive structure, but was done with the

emphasis on user satisfaction. Where this project has benefited is from

the different types of knowledge sources. There is often claimed to be a

substantial gap between an academic concept of professional knowledge

(and therefore what is taught to students) and the actual skills required in

the field. As Schon has suggested there exists a core of "professional

artistry" which separates good practitioners from bad (Schon ?). The field

of die-casting exhibits many aspects of the science/artistry divide. Whilst

rigourous equations exist to calculate optimum values and so forth, there

is also a wealth of knowledge to be gained that is not so easily definable.

This is the craft knowledge and it is in this area that much of the non-

verbalisable knowledge lies. The task of verification and testing by a

knowledgeable pragmatist user can be seen as the first step towards

eliciting this knowledge. However it may be that conventional expert

system techniques will not be sufficient to cope with the knowledge and a

possible hybrid approach will be required. It is in this area that our

research is now focusing. The system is currently being developed to

incorporate the suggestions gained during the validation period.

It is our experience that whilst purely ad hoc solutions do not advance

expert system development as a whole, any model must incorporate a

suitable amount of flexibility, and that what is apt for one domain, or even

one expert within one domain, may not be so for another. The most

important factor in constructing a worthwhile expert system would seem to

be the cooperation and enthusiasm of the expert(s), and in order to gain

this there may have to be sacrifices with regards to pure knowledge

engineering theory.



REFERENCES

1. Mertz, A. Pressure Die Casting Defects: Measures to Avoid

Arbeitsgemeinschaft Metallguss, Aalen, 1991.

2. Johnson, L. & Johnson, N. 'Knowledge Elicitation Involving

Teachback Interviewing' Knowledge Elicitation for Expert Systems - A

Practical Handbook, ed. Kidd, A. Plenum Press, 1985.

3. McGraw, K. L. & Searle, M. R. 'Knowledge Elicitation with Multiple

Experts: Considerations and Techniques.' AI Review, Vol 2, pp. 31-44,

1988.

4. Ling, X. & Rudd, W. G. 'Combining Opinions from Several Experts.'

Applied Artificial Intelligence, Vol 3, pp. 439-452, 1989.

5. Walkington, W. G. & Bruner, R. W. Die Casting Defects. The Society

of Die Casting Engineers, Illinois, 1987.

6. Sell, P. S. Expert Systems: A Practical Introduction. Macmillan, London,

1985.

7. Schon, D. A. Educating the Reflective Practitioner. Jossey-Bass, 1991.


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