Development of a knowledge-based system for materials management

Materials and Design 19 Ž1998. 39]56

Development of a knowledge-based system for materials management K.R. Trethewey a,U , R.J.K. Wooda , Y. Puget a , P.R. Robergeb a

b

Department of Engineering Materials, Uni¨ ersity of Southampton, Highfield, Southampton SO17 1BJ, UK Department of Chemistry and Chemical Engineering, Royal Military College of Canada, Kingston, Ontario, Canada K7K 5L0 Received 7 February 1998; accepted 9 March 1998

Abstract In the past, many mistakes have been made in selecting the best materials for a given task. Thus, tools for humans to optimise the selection of materials will be valuable assets, particularly when the field of application is broad, the problem complex, the operating envelope variable, or the environment is aggressive. In this paper, a methodology for construction of a generic computer materials selector is described. A knowledge structure is presented in which materials selection and failure analysis are at opposite ends of a spectrum of materials performance. An example of the selection of a coating for marine use is given. Besides being of great value to designers, the tool is of considerable potential use for general materials information systems and computer-based learning modules. Q 1998 Elsevier Science Ltd. All rights reserved. Keywords: Knowledge-based system; Materials selection; Failure analysis; Corrosion; Materials performance; Engineering design; Marine coatings; Seawater

1. Introduction The explosion of computing power in the 1980s, combined with the provision of enormous machine resources on the desktop of the 1990s, offers an attractive solution for those involved in complex problem solving. In software, there was also, in the 1980s, a high level of excitement towards artificial intelligence tools. Unfortunately, early knowledge-based systems ŽKBSs. in engineering often failed to live up to expectations. This situation resulted in a general loss of confidence by users that is proving difficult to overcome. Now that the reality of the situation is better understood and many information processing tools have progressed to a

U

Corresponding author. Tel.: q44 1703 593279; fax: q44 1703 593016; e-mail: [email protected] 0261-3069r98r$19.00 Q 1998 Elsevier Science Ltd. All rights reserved. PII S0261-3069Ž98. 00010-7

second or third generation, the potential for effective KBSs in the workplace is undeniable. Recent papers have outlined the present situation and described the methodologies for creation of a new family of KBSs based upon the latest object-oriented software w1]3x. This paper develops these ideas further and focuses them into an application for the selection of coatings for superior performance of pipes carrying seawater. It describes the construction of a KBS for the selection of a suitable coating material by a generic approach that could be extended to any problem associated with the selection of materials for usage in difficult environments. This is achieved by setting the problem in the context of a wider description of a model of materials performance in which the first stage is materials selection and the last stage is failure analysis. The perspective of this materials performance spectrum is shown to be a powerful tool for understanding

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

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the complex interactions of materials environments and people, and for computerising materials performance expertise.

In its simplest form, a KBS is a combination of a database and an inference engine. Fig. 1 illustrates the process of building a KBS. The transfer of knowledge from an expert to a computer is rarely straightforward, for experts often have a limited understanding of how they perform the complex tasks readily associated with their expertise. In this context, the expertise elicitation shell indicated in Fig. 1 can be described as a system to assist the transfer of expert knowledge to a computer.

field of hypermedia techniques. Electronically stored data is increasingly upgradeable and compatible across different platforms w5x. When a document is added to a hypermedia system, information is normally added to it to define the links } a process known as ‘mark-up’. When large quantities of information and documents are involved, not only is much effort required in the initial mark-up phase, but subsequent modification is extremely timeconsuming. Therefore other methods with even greater flexibility to upgrade have been sought. Research has been carried out to develop open architecture hypermedia systems w6,7x in which original documents are not changed and the link information is created and stored in a way which retains data accessibility, allowing it to be distributed across different networks and hardware platforms.

2.1. Knowledge-bases, databases and hypermedia

2.2. Inference engine

2. KBSs and their construction

The distinction between a knowledge-base and a database is still unclear. Most would probably agree that use of the word ‘knowledge’ implies a higher level of information compared with ‘data’ which is frequently just stand-alone numerical information, but which is becoming more and more textual. Knowledge is mostly expressed in plain language, but also involves rules and relationships by which the ‘data content’ of that knowledge can be considered to interact. The structure of the knowledge-base is more important than its content, which, as we shall see, may be quite ill-defined. Therefore this work is sharply focused on the structuring of corrosion and materials performance knowledge. All useful KBSs require data. There are many ways of creating databases and an excellent paper by Angwin w4x in the context of corrosion and materials performance provides details of some of the most successful methods to date. In the near future, databases will become much larger because of developments in the

The mechanism by which databases are interrogated, the inference engine, is also the subject of intensive research. Many systems were created in the 1980s using PROLOG and LISP. New PROLOG-based engines that can interrogate open architecture hypermedia databases are the subject of research at the University of Southampton w5x. However, object-oriented programming languages have provided user-friendly tools for creating powerful and flexible code for use in KBSs. The work described here has been carried out using Microsoft Visual Basic to create the inference engine and Microsoft Access for the database. 2.3. KBS function Simple retrieval of the knowledge is of little value without a framework in which to store it within the knowledge-base. The knowledge-base must not only be constructed with this in mind, but also with some plan of how it is to be queried by an inference engine. One powerful technique that could be adopted is case-based reasoning ŽCBR. which often closest represents what actually goes on in failure analysis w8]11x. It mimics the human expert who, employed by a client with a failure, conducts an interview to determine the failure mode. During the interview } essentially a question-and-answer session } the expert naturally seeks precise details of materials, environments, operating conditions, etc., but usually widens his enquiries to include such aspects as: v

v

Fig. 1. Schematic diagram for the process of building a KBS.

Were there any undocumented excursions from the prescribed operational envelope? What was the frequency of failures in other batches or equivalent systems?

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

Often, the expert elucidates factors which the client did not consider important; some interaction by an operatorruserrmaintainer at an earlier stage in the history of the system may have played a large part which the client Žnot being expert. had discounted. Alternatively, there may be an interaction from a remote system not considered part of the one in question. The role of the expert in this way is shown in Fig. 2. After a comparison of cases from the experience of the expert, an answer is suggested as likely. A short sequence of additional questions may then provide corroborating evidence from which the expert can then make a final assessment. At this stage, depending upon the degree of success achieved, he can, if necessary, calculate a probability of being correct, or the likelihood of one mechanism rather than another. He then provides decisions regarding the nature and mechanism of the failure and the remedial actions necessary. Technically speaking Žand sometimes without realising it., the expert may have redefined the ‘system’ to include a wide range of other parameters and people before reaching his conclusion. Many non-expert failure analyses reach the wrong conclusions because they either fail to define the system adequately or they consider a case in isolation from a history containing other related or similar cases w3x. 2.4. KBS construction

Construction of a failure analysis KBS to mimic the human expert’s knowledge meets a number of obstacles.

v

41

How does the expert decide which questions he should ask and in what order?

In fact, this is not a difficult problem. Early expert systems followed a flow chart of questions derived from forward chaining through a knowledge tree. This was obviously the most suitable way of computerising the process when computer code was wholly based on sequential instructions. Object-oriented languages allow great flexibility to investigate and analyse knowledge sets, moving through a domain according to which sets are incomplete and according to the actual information in those sets. Answers to any given question frequently determine the one that follows. v

How do you cope with answers that contain errors or imprecise information?

Humans are used to working with imprecise information. They naturally accept vague use of language, making continuous interpretations of the information they receive based upon context. Occasionally, ambiguity causes problems, but, providing the scope for ambiguity is realised, a question and answer clarifies the matter. For a machine to carry out the same tasks, unambiguous language must be defined. This is the first event in the creation of a traditional programming language such as FORTRAN, PASCAL or C. The structuring of knowledge inevitably requires the definition of terms, statements of relationships between them and elimination of ambiguity. To overcome this problem, modern mathematics has devised techniques to handle uncertainties and fuzziness, which allow processing of imprecise or ambiguous information. v

How do you ensure that the database of knowledge is adequate, i.e. that the ‘experience’ of the expert is sufficiently broad to encompass the failure in question?

This can only be accomplished by extensive testing of the KBS. Ideally, a good KBS should incorporate the expertise of many experts, at which time it might be expected to perform better than any one human should. 2.5. Mathematical approaches

Fig. 2. Failure analysis by case-based reasoning.

A particularly good exposition of computational strategies in the building of KBSs can be found in the text by Durkin w12x. In general, three approaches are possible. In the first, a Bayesian approach for application of probability theory to inexact reasoning is employed. This is a powerful and precise method that has been used in such diverse fields as weather forecasting,

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K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

financial planning and mineral exploitation. However, it has the limitation that the sample space must be well defined and probabilities of any event are required from sets of past data. Therefore whilst this approach is a good one for well-known statistical environments, reliable statistical information is unavailable for many real-world problems and the Bayesian approach is inappropriate. The second technique employs the now well-established theory of fuzzy logic. This highly-developed method employs well-defined and often complex mathematical relationships for description of the sets and the rules of combination. Fuzzy logic was considered inappropriate in the context of this work because the degrees of certainty of the knowledge in the knowledge-base are insufficient. In other words, an expert is not able to define his knowledge in a way that satisfies the quantitative criteria for fuzzy logic techniques. The third method and the one employed here overcomes these deficiencies by use of Certainty Theory which provides a judgmental approach for deriving inexact inferences w13x. A pivotal parameter is the ‘measure of belief’ ŽMB., which is a number reflecting an expert’s increased belief in a hypothesis, based on some evidence. An inverse is the ‘measure of disbelief’ ŽMD., a number reflecting an expert’s increased disbelief in a hypothesis, based on some evidence. From these, a ‘certainty factor’ ŽCF. is a measure of net belief according to CFs MB y MD. Rules can be employed for manipulating these parameters and the CF can be used to direct a search into promising areas or terminate if an approach is unlikely.

3. Knowledge structure of system performance 3.1. A model of materials performance and management For decades, materials scientists have devoted much energy to the classification of materials and values of their properties, but algorithmic strategies for the selection of materials by computer have been rare. Indeed, in many industries, the selection of materials has been an evolutionary, iterative process based on:

upon the engineering community and has resulted in a powerful computer tool w14x, as well as a definitive text w15x. It is an excellent example of algorithmic method and has been influential in evolving some of the ideas presented here. Fig. 3 is based on Ashby’s analysis and illustrates schematically the stages of materials selection, but extends well beyond the scope of materials selection alone. After all, materials should not be selected only for some initial exposure. Selection should include performance evaluations over a specified lifetime. In the widest context of engineering systems, materials function in a great variety of environments, whether physical, mechanical, chemicalrbiological, or human. For example, in traditional corrosion engineering, the specific criterion for a corrosion failure is a chemical environment. The human environment is often considered to be implicit and consequently ignored. This leads to a different perspective in the analysis of corrosion failures, has implications for the design of systems and is the main reason why the cost of corrosion has remained high in many industries w2x. 3.2. Definitions: language ¨ s. knowledge To distil decades of human engineering knowledge and experience into a computer, the knowledge-base must be both flexible and comprehensive yet concise. This kind of effort was usually left to educators whose aim was to teach more clearly the essence of engineering to students via lectures or textbooks. Now there is a more pressing reason, for no known computer method will be successful without a knowledge structure. Once this generic framework has been defined, the way is open for a new generation of computer tools, whether for use in education or industry. Definitions of language are important but still inconsistent throughout the engineering industry: professional engineers in different countries use many identical terms in different ways. Some terms are simply ambiguous or vague. Engineers from the same culture and background will describe the same failure in slightly different language. Here the work of the various standards organisations has been a vital contribution. Whereas today’s computers contain numerous dictionaries and thesauruses placed there to help users with language, tomorrow’s computers will contain similar

1. application of existing knowledge and experience to make a selection; 2. testing and development in new applications and environments; and 3. feedback to the design process of the lessons learned for improved materials selection next time around. In recent years, an innovative approach to the process of materials selection has made a considerable impact

Fig. 3. Stages in the selection of materials.

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

tools of technical language placed there to help the computer more efficiently perform human-like actions. Computers will need large databanks of synonyms and colloquial data to enable them to ‘understand’ data relating to complex situations given to them by users. Indeed, computers are already exhibiting the ability to identify and correct language mistakes by users, as is evidenced in the ‘smart’ capabilities of the Microsoft WordTM software package, for example. Rather like the original creation of tables of logarithms, or the gargantuan task to unravel biochemical code of the human genome, these tasks need only be performed once, yet much of the work has already been done in paper format and only the task of computerisation remains. This is readily achievable using modern scanning software. 3.3. Functional quality Materials performance is a time-dependent function involving the interaction of materials and environments. It has already been suggested that materials management should be the term to describe the combined effects of human interactions and materials performance w2x, but it is still difficult to find engineering studies which specifically include human actions. One major study by Bea w16x has attempted this and, in so doing, has used the term quality in an engineering sense as a synonym for materials management. This paper develops this idea further by use of the term functional quality, so as to avoid confusion with other meanings of the word quality in the usual management contexts. One thesis is that the scientific approach to engineering is a ‘bottom-up’ study of detailed mechanisms which is considered not to need the influence of humans. Systems engineers, however, prefer the ‘topdown’ approach that broadens the definition of the system and is more likely to include humans. This is consistent with the lessons to be learned from the well-known UK report on corrosion w17x which said that corrosion control of even small components could result in major cost savings because of the effect on systems rather than just the components. The functional quality of a system is a statement of the likelihood of that system completing the mission for which it was designed. The link between ‘measure of belief’ and plain language means that it could be expressed either as words or numbers Že.g. probability..

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Fig. 4. Schematic diagram showing a formalised structure at the top of the knowledge tree for functional quality.

tional quality, the materials performance and the people factor. This is consistent with the strategy proposed elsewhere in the context of corrosion, but which generalises to all materials performance issues, Fig. 5, where materials management is deemed to be a wider context that explicitly includes the people factor w2x. When people factors are excluded from consideration the problem reduces to one of materials performance with a materials factor and an environment factor. 3.4.1. Material factors Table 1 contains properties drawn from a recently published NACE International Standard on Formats for Electrochemical Data prepared by the T-3U-4 working group w18,19x. In this standard, a full set of information is provided regarding a statement of metallic materials for electrochemical polarization curve measurements such that, when data files are exchanged between laboratories, the results are meaningful. Such a statement of properties is a valuable distillation of many years of combined corrosion expertise of NACE members.

3.4. Knowledge trees In this research, a knowledge structure or tree has been devised as shown in Fig. 4. Adopting the top-down approach, the top level has functional quality as its first parameter. Two factors are considered to affect func-

Fig. 5. Schematic diagram showing the relationships between the parameters contributing to functional quality.

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

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Table 1 Specification of material Žmetallic. factor in object-oriented format Object

Factor

Comment

Material

Class

To which class does the material belong? ŽCoarse and fine subdivisions may be necessary.. What is the common name of the material? What is its international designation? What is the heat treatment or other specification associated with this material? What shape was the material supplied in? What method was used to produce the material? Is there an identification associated with the lot supplied? What is the chemical analysis? What is the material density? What metallurgical condition was it supplied in? What is the % cold work done on the material? What is the material tensile strength? What is the yield strength of the material? What percentage offset was used to determine the yield strength? What is the hardness of the specimen on a recognised scale? What was the surface finish of the supplied material? What surface treatment was the material given either before or after supply?

Common name Designation Specification Shape Production method Lot ID Chemical analysis Density Condition Cold work Tensile strength Yield strength Percent offset Hardness Surface finish Surface treatment

3.4.2. En¨ ironmental factors Similarly, valuable information about properties of the environment is contained in the same document and has been used, in part, to construct Table 2. Parallel work such as this is presently being conducted by ASTM G01.03 working group on computers in corrosion w19,20x. 3.4.3. People factors Table 3 brings together a summary of the interactions which people make which, so far, in a corrosion context, has been presented only by us w2,19x. If the people factor branch of the tree is expanded, Fig. 6 results w21,16x. Alternatively, moving further down the tree and considering materials performance at level 2, Fig. 7 for the material factor results. A similar tree would apply for the environmental factor. These knowledge trees are critical both in the taxonomies of materials performance knowledge and in providing the mechanism for ultimate calculation of quantities with Certainty Theory. This will be explained in detail below. 3.5. Processes leading to failure Materials selection is the first stage of a history of system performance that culminates at the expiration of the design lifetime and a good functional quality. Fig. 8 is a diagram of an empirical model of system performance. In this figure, system performance has been equated to materials performance. System refers to the

combination of materials, environments and people associated with its lifetime. The perspective of materials selection is a view of this figure from left to right. During the lifetime of the system, influences operate on it to produce effects. At intervals throughout the lifetime, a diagnosis is made of the materials performance. This diagnosis is, in principle, ‘Has the system changed?’ If the answer is no, we conclude that materials performance has been good, since it must be in the same state as when its life began Žthe initial state assumed to be good.. A good degree of functional quality exists in this case. If the system has changed beyond anything allowed for in the specification, system performance and functional quality are poor. If the effect is a departure from specification, we refer to a defect having occurred; if it is an inability of the system to perform according to specification, we refer to a fault. Failure is the termination of the ability of the system to perform according to specification and the failed state is the state of the system in which it is unable to perform according to specification. When the system has failed, the failure mode is the mechanism leading to the failed state. Failure analysis is the process of determining the failure mode. Its perspective is of Fig. 8 from right to left. Fig. 9 is a classification scheme for the way people, environments and materials interact. Each of the commonly considered environments exert characteristic influences; the physical environment has influences of electricity, magnetism and radiation, for example, whilst

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56 Table 2 Specification of environment factor in object-oriented format Object Žadjacent phase.

Factor

Comment

Liquid

Composition intrinsic

What is the nature of the liquid? What is the composition in terms of dissolved gases or suspended solids? What is the pH of the liquid? What is the temperature of the liquid? Did the liquid possess velocity relative to the material? Is the liquid under an applied pressure? What is the conductivity of the liquid? Is there a current flow in the electrolyte from an external source? Is the material immersed in the liquid or splashed by it?

Composition extrinsic

pH Temperature Relative velocity

Pressure Electrical conductivity Electrical conduction

Extent

Solid

Composition intrinsic Composition extrinsic

Temperature

Relative velocity

Load condition

Shape Extent

Contact

Gas

Composition intrinsic Composition extrinsic Relative velocity

Temperature Pressure Extent

What is the nature of the solid? Is there a surface coating or passive film on the solid? What is the operating temperature Žrange. of the solid? Is there a relative velocity between the solid and the material? Is the solid under an applied load? Does it exert a pressure on the material? What is the geometry of the solid? What is the magnitude of the coverage of the material by the solid? Describe the degree of contact between the two solid phases. What is the chemical composition of the gas? Are there suspended particles such as smoke? Is there any velocity of the gas relative to the material above the normal molecular diffusion? What is the temperature Žrange. of the gas? Does the gas exert pressure on the material? Does the gas completely envelop the material?

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it is largely the composition, temperature and pressure which are dominant in the chemical environment. Traditional ‘bottom-up’ science and engineering focuses on the materialsrenvironments interactions but does not generally consider the human factor as part of a system performance model. Arguably, it was a UK Government Committee report w17x which first identified the human factor as significantly contributing to the continuing high costs of corrosion. 3.6. The diagnosis of failure This work is built upon accepted standards and definitions of the ASTM, which groups its publications into seven categories of mechanical failure: 1. 2. 3. 4. 5. 6. 7.

fracture, deformation, creep, wear and erosion, fatigue, surface damage and corrosion.

By defining precisely the attributes of a system that exhibits one of these failure modes, we can structure the knowledge in the database and determine what information is known, what is not known and what needs to be known. 3.7. Dealing with practical knowledge: the path from effect to diagnosis In the creation of a KBS, the steps from concept to computer are well established. Today’s relational databases on desktop PC platforms are extremely powerful and well able to cope with the type of system described here. Knowledge is arranged in tabular form using a modern relational database software package which can make any table transparent to any other. In an object-oriented KBS, tables such as Tables 1]4 are vital precursors to framing the sequence of questions and interpreting the answers provided. The KBS builds a model of the system and its performance history by identifying as many factors as possible, i.e. material factors, environment factors, human factors, adjacent phases, influences and effects. In failure analysis, knowledge of these factors leads towards a diagnosis } the failure mode. In a materials selection problem, it suggests the optimum combination of materials properties to cope with the design specification. Two questions are relevant at this stage. What is the complete set of knowledge to be certain of the failure mode? How do we process this knowledge in order to rank the effects that we expect? Answers to both questions are goals of this research. Obviously, humans

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

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Table 3 Sample specification of people factor in object-oriented format Object

Factor

Comment

Procurer

Main specification Budget

What is the main system being specified? Did the budget introduce compromise into the design?

Designer

Sub-specification Materials specification Environment specification

What is the sub-system being specified? What is the optimum materials selection? Has the correct definition of the operating environment been applied?

Manufacturer

Materials

Were the same materials used in manufacture as were originally specified? Did the purchased starting materials conform to the specification in the order? Have the most suitable joining techniques been employed?

Supply Joining

Installer

Installation Set up

Maintainer

Maintenance schedule Replacement parts Maintenance procedures

User

Operational envelope History Shutdown Malpractice

Fig. 6. Knowledge tree for functional quality showing the people factors Žcompare with Fig. 4..

Has the system been installed according to specification? Has the correct setting-to-work procedure been followed? Has the correct maintenance schedule been followed? Have the correct spares been used in repairs? Have the correct maintenance procedures been carried out? Has the system been used within the specified conditions? Is there a history of similar failures or is this an isolated occurrence? Do aggravating conditions exist when the system is not in use? Is there any evidence that unauthorised personnel have abused the system?

Fig. 7. Knowledge tree for materials performance showing the material factors Žcompare with Fig. 4..

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

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Fig. 8. Empirical model of system performance.

would quickly become disillusioned if a KBS endlessly sought information that the human felt unnecessary. To some extent, this question remains unresolved. The strategy adopted is, wherever possible, to consult recognised experts and to computerise their knowledge by means of structured elicitation exercises. There is no doubt that satisfactory KBS functionality will not be achieved without first constructing a comprehensive taxonomy of materials performance knowledge. Use of Certainty Theory provides a good mechanism for dealing with the second question. In some situations an expert providing knowledge about a process which has a positive sense will also imply information

Fig. 9. Classification scheme for environmental influences.

about other processes which is in the negative sense. A simple example is if an expert is providing knowledge leading to the conclusion that a failure was caused by pitting corrosion, he is also implicitly minimising the direct impact general corrosion had on the situation. Thus when an index is increasing positively for a pitting corrosion mechanism, it is also increasing negatively

Table 4 Summary of environments, influences and diagnoses for materials performance Environment

Adjacent phase

Influence

Label

Diagnosis-1

Diagnosis-2

Diagnosis-3

Chemical

Solid Liquid Gas

Composition Composition Composition

d1 d2 d3

Chemical attack Chemical attack Chemical attack

Corrosion Corrosion Corrosion

Environmental damage Environmental damage Environmental damage

Mechanical

Solid

Impact load Shear load Abrasive load Fatigue load Abrasive load Impact load Impact load

d4 d5 d6 d7 d8 d9 d10

Fracture Fracture Wear Fatigue Erosion Fracture Fracture

Deformation Deformation Erosion

Impact failure Shear failure

Wear Deformation Deformation

Surface damage Impact failure Impact failure

Magnetism Radiation Temperature Pressure Electricity

d11 d12 d13 d14 d15

Deformation Implosion

Overheating Fracture

Temperature Radiation Pressure Temperature Radiation Pressure

d16 d17 d18 d19 d20 d21

Magnetic damage Radiation damage Creep Explosion Electrical Breakdown Creep Radiation damage Explosion Creep Radiation damage Explosion

Deformation

Overheating

Implosive Deformation

Fracture Overheating

Composition Composition Composition

d22 d23 d24

Biofouling Biofouling Biofouling

Liquid Gas Physical

Solid

Liquid

Gas

Biological

Solid Liquid Gas

Implosion Environmental damage Environmental damage Environmental damage

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K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

for general corrosion. A KBS can follow all these indexes during the course of an analysis: some will be positive and some negative. When a certainty factor for any given diagnosis passes an arbitrary threshold, the KBS can say that it has arrived at a diagnosis, just as the human gradually forms a conclusion over time. For comparison, the KBS can also quote certainty factors of other failure modes and, very usefully, it can also say which modes are not likely. Ultimately, it is envisaged that CBR must be incorporated into an effective KBS, although this is not developed further in this work.

4. Materials selection processes The way in which a human selects a material for an application is a complex matter and many writers have attempted to define precisely the stages involved, in such a way as might be converted into an algorithm for a computerised methodology. Recently, a method was published by Ashby w15x which has been influential and may yet prove to be adopted as standard. The scheme described here attempts to build upon that work, but approaches the task in three stages ŽFig. 3.:

v

v

v

Stage I considers the task carefully and identifies the most likely way in which the component will ultimately fail. In this paper, the failure mode is called a diagnosis. Stage II identifies the material properties most relevant to each diagnosis; and Stage III optimises the properties for best resistance along the path to that diagnosis.

Ideally, a lifetime for the component within the system specification will be known. At another level, factors such as cost, availability and fabrication methods enter the methodology. As pointed out by Ashby w15x, a significant problem is that, whilst we generally make materials selection decisions on the basis of materials properties, most tasks require an optimisation of more than one property. One of the most obvious examples is the strength-toweight ratio of materials in high performance applications. The task demands optimisation, not of strength or density properties alone, but of a ratio of the two. Thus, whilst strength and density remain fundamental properties defined by a standard test and measured precisely in laboratories, strength-to-weight ratio might be called a complex property composed of more than

Table 5 The application Substrate Nature

Geometry Žpipework.

Environment Seawater

Suspended particles

Coatings System type Thickness Typical conditions Antifouling

Carbon steels Žmild steel. CurNi Ž90r10 and 70r30. Nickel aluminium bronze Stainless steels Bore Žinternal diameter. Length Wall thickness, t, with nominal working pressure, p CurNi

; 100 mm 1]2 m

Steel

t s 7.5 mm p s 69 bar or 1000 psi t s 1]3 mm p s 10 bar or 145 psi t s 2]4 mm p s 60 bar or 870 psi t s 5.4 mm

Natural seawater Current flow rate Desired flow rate Design requirement Typical size Sand concentration

pHs 7]8.5 1]3 mrs 10 mrs 4.5 mrs - 50 m m - 10 ppm

Unrestricted Unrestricted Cyclic pressure up to 70 bar Cyclic temperature 0]608C Required

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

one fundamental property and not directly measurable itself. The immediate attraction of the computer therefore is its power to instantly assess fundamental properties in a complex way and provide ‘on-the-fly’ assessments of combinations of fundamental properties. This has been achieved in a powerful software tool w14x which relies upon a very large database of materials and numeric values of fundamental properties. The Ashby approach breaks down in many situations, notably coatings technology, when numerical values of desirable properties are not available. In situations such as these, materials selection tasks are accomplished based upon depth of experience of those humans making the decisions. It is here that inappropriate materials may be selected when, for example, the designer has limited experience or when the operational envelope of a given system is on the edge of a particular knowledge domain. One of the main aims of this work is to consider the scope for a new family of KBSs that can work with less certain domain knowledge but still offer the best advice on materials selection decisions. As an example, the selection of a polymeric coating for flowing seawater service has been chosen and will hereafter be referred to as the application.

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6. Stage I. Definition of the task

failure modes but include synonyms that assist by broadening the language to include terms in everyday usage. Corrosion, for example, has its own specific definitions, but in other situations, terms such as chemical attack or environmental damage might be more appropriate. Similarly, the adjectives used to describe the failure are quite variable, dependent on both user and context. Table 6 is a comprehensive list of adjectives used to describe failed systems. The table was constructed on the basis of two authoritative dictionaries and each line of the table has an individual definition Žnot shown.. Synonyms fitting each definition have been grouped on the same lines and an assessment has been made as to which of the seven ASTM failure modes applies. In this way, links from common language to standard terms can be made. It is emphasised that these links have intrinsic quantifiable certainty associated with them, just as with human language usage. For the application, the same failure modes are relevant and may contribute to the overall probability of failure in varying degrees. Fig. 10 summarises this by means of a knowledge tree. Here, coated surfaces are identified as clad, dipped, electrodeposited, sprayed, anodised and painted. Each of these will have its associated failure modes. For painted surfaces, they have been identified as the same seven ASTM fundamental modes, replacing corrosion with environmental damage. Comparing the scheme shown in Fig. 10 with the available diagnoses listed in Table 4 shows that diagnosis d18 applies uniquely to fracture, whilst d16 could be applied to both creep and deformation. Diagnoses d8 and d9 both apply to surface damage and to wearrerosion, whilst d2 and d23 both apply uniquely to environmental damage. For a coating in seawater, only those diagnoses pertaining to the liquid environment Ž7 out of 23. need to be considered, i.e. d2, d8, d9, d16, d17, d18 and d23. The relative importance of each mode is now determined by a process called knowledge elicitation. Early in the computerisation process, expertise from a domain expert is stored in a knowledge-base in an interviewing exercise. Questions are posed to the expert about the likelihood of various failure modes, thus:

Part of the definition of the task is to examine the specification and obtain an understanding of the most likely cause of failure. Table 4 summarises the environments, influences and failure diagnoses to which an engineering system is susceptible. Seven fundamental failure modes have been defined by ASTM, i.e. corrosion, creep, deformation, fatigue, fracture, surface damage and wearrerosion. Diagnoses are based upon

Question 1: ‘ How important is the {increasing r decreasing} effect of {influence} of the {adjacent phase} on the {type} {component}, such as would lead to {diagnosis-1}, {diagnosis-2}...?’ The computer moves stepwise through the list and inserts the relevant terms for each combination of environment, influence and diagnosis. In the application, for d9, this translates into:

5. An example application In the offshore industry and ships, many pipes handle seawater, mainly for cooling purposes. The aggressive marine environment often carries suspended sand particles and exposes the pipes to external factors inducing a combination of erosion and corrosion on their surface. This phenomenon leads to final failure of the pipes by perforation after an average lifetime of 7 years. To extend pipe system life and to reduce costs, internally coated mild steel pipe systems are being considered for this duty. If a coating is continuous, as well as chemically inert and electrically insulating, corrosion of the substrate cannot take place. After consultation with industry, a specification for the application was prepared. This is listed fully in Table 5, a task to select a suitable coating material for superior pipe performance in seawater.

50

Table 6 List of adjectives used to describe failed systems Word 2

Alligatored Blushed Broken Chalked Checked Corroded Cracked Curved Damaged Defective Deformed Delaminated Digested Discoloured Exploded Fouled Fractured Holidayed Imploded Kinked Melted Parted Penetrated Pitted Protruded Rubbed Ruptured Rusted Scaled Scratched Seized Spalled Sulphidated Swelled Tuberculated Wasted Weathered

Cracked Bloomed Cut

Word 3

Fractured

Word 4

Fragmented

Word 5

Corrosion

U

U U

U

U

U U

U U

U U

U U U

U

U

U

U

U

U

U

U

U

U Veined Burnt Fissured Crooked Deteriorated

Bent Separated Decomposed Stained Shattered

Distorted Disbonded Consumed Tarnished Disintegrated

Eaten Faded Detonated

Coloured Blasted

Broken

Sheared

Shattered

Parted

Collapsed Warped Liquified

Crushed Twisted Fused

Shattered Bowed Fluxed

Distinegrated Rolled Frozen

Oxidised Torn Flawed

Dilapidated

Flowed

Wrinkled

Holed Dimpled Bulged

Breached Cratered Swollen

Pierced Dented Creased

Punctured Pocked Ridged

Split

Divided

Cleft

Ripped

Furred Marked Jammed Worn

U U U

U U Scored Fused Galled

Gouged Bound Abraded

Fatigue

U

Rift

Crazed Dissolved Split Angle Degraded

Fracture

U U

Creep

U U U

U U U U U

U U U U U

U U U U U U

U

U U

U

U

Grooved U

U

Gouged U

Bubbled U U

Surface damage

U U

U U

Wearrerosion

U U

U

Worn

Deformation

U

U

U U U

U

U

U

U U U U

U U U U U U U U U

K.R. Trethewey et al. r Materials and Design 19 (1998) 39]56

Word 1

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The knowledge engineer might feel that the expert would find it easier to give his answers in percentages that are subsequently converted by the computer. Perhaps ideally, the expert answers directly on the computer and in plain language. Thus, the expert selects his answer to a question from a plain language menu:

Fig. 10. Knowledge tree for coatings failure.

‘How important is the increasing effect of impact load of the seawater on the polymeric coating, such as would lead to fracture, deformation or impact failure?’ It is essential to maintain the credibility of the KBS in the eyes of the human with a process called filtering. For example, more advanced systems will have carried out filtering actions based on previously defined descriptions of the problem so that irrelevant questions are not asked, causing users to lose patience with the computer. In the application, our KBS deals only with combinations involving liquid Žseawater.. Solid Že.g. sand particles. and gas Ži.e. air. as an adjacent phase have been filtered out for simplicity. Other more complicated applications would require consideration of these diagnoses too. The computer carries out a process of filtering to determine how many questions it will ask and to keep the human informed about progress through the interview. A human who does not know how long the interview will take or how may questions hershe will be asked will lose confidence very quickly. Furthermore, the knowledge elicitation process can be quite arduous, even for a well-defined problem, and great care must be exercised by the knowledge engineer to maintain the support of his expert. Several mathematical strategies are possible for KBS function, such as those based on probability, fuzzy logic and Certainty Theory. The first two are not used here. Certainty Theory was adopted for this paper. Rules can be employed for manipulating these parameters and the CF factor can be used to direct a search into promising areas or terminate if an approach is unlikely. Certainty Theory requires that the expert’s answers be converted to a scale for MB from q1 to 0, with q1 representing complete belief. Using the MD index, the range would be from 0 to y1, with y1 representing complete disbelief. An expert could be given a paper questionnaire and asked to insert numbers into a table.

‘Definitely, almost certainly... Probably not, definitely not’. Table 7 lists the available plain language descriptors and associated index values. Thus, attached to each plain language selection, but invisible to the expert, is a CF index value which the computer: Ži. stores in a new table to hold the results of the knowledge elicitation interview; and Žii. processes in further steps. It should be noted that index values ) 0.8 are not used here because of a problem called saturation in which, if a value of 1.0 is used, the total certainty propagates through the KBS and gives poor discrimination between possible outcomes. Further discussion on the subject of saturation is beyond the scope of this paper. The apparent simplicity of such a scheme is deceiving. The phrasing of the question is vital in eliciting the best answers from experts. If possible, a good elicitation will reduce ambiguities by the use of clarification or ‘advice’ statements, again drawn from the database tables. When the elicitation exercise is carried out, experts find the process very demanding, even though the questions and answers appear simple. An elicitation exercise necessarily draws upon the entire spectrum of the knowledge domain. Experts need to call upon every part of their experience to provide what they consider a satisfactory answer and often realise their own deficiencies in some areas of the domain. Again, filtering is important so that the knowledge domain is not too broad and it is usually necessary to consult with more than one expert to obtain a more complete expression of the domain knowledge. When faced with a lengthy elicitation interview, experts may need encouragement so that they feel they are contributing usefully, even though they may be dissatisfied with their answers. Table 7 Certainty factors Žafter Durkin. Plain language descriptor

Certainty factor

Definitely not Almost certainly not Probably not Maybe not Unknown Maybe Probably Almost certainly Definitely

y1.0 y0.8 y0.6 y0.4 y0.2 to q0.2 0.4 0.6 0.8 1.0

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6.1. Rules from uncertain knowledge In Certainty Theory, rules are defined in which evidence, E, contributes to a hypothesis, H. Thus, using diagnosis d9 as an example, the evidence might be: there is an increasing effect of impact load of the seawater; and the hypothesis might be: the polymeric coating will fail by fracture, deformation or impact failure. In general, therefore, we make this definition: RULE 1: IF E1 THEN H; Certainty Factor s CF1. In the application, this translates to: IF: THEN:

there is an {increasing} effect of {Influence} of the {Adjacent Phase} the {Type} {Component} will fail by {Diagnosis-1}, {Diagnosis-2}...

CERTAINTY FACTOR: CF1 (from Table 7). Table 8 lists the results of an interview for the application. Notice that, from a list of 24 possible diagnoses ŽTable 4., the filtering process reduces these to just seven. It can be seen from Table 5 that, in a flowing seawater situation, wear and erosion has been assessed as the greatest risk to a coating with environmental damage also a strong possibility. Temperature effects are not normally considered a problem unless the water rises consistently above 608C.

w14x. Table 9 is a summary of all properties that may be relevant to materials performance. The list was compiled in conjunction with the properties contained in the Cambridge Materials Selector and developed slightly to relate more closely to this work. Thus, for example, two classes of property were identified: first, those that are intrinsic to all materials and which might be considered fundamental in all applications; second, those properties that are dependent on application. Within each of these two classes, types of property were identified in order to assist in the logical structuring. As the example below will show, the list is probably not exhaustive and requires development for each application. The properties are denoted by p k , for 1 - k - q. Fig. 11 shows how properties are present in the relevant part of a knowledge tree for failure of a paint coating by wear and erosion. Although the same property groups are used for all diagnoses, different groups will have different CFs depending on the diagnosis being considered. For the second phase of the task in which the importance of properties is considered, a new rule is defined thus: RULE 2: IF E2 THEN H; Certainty Factor s CF2. Notice that the same hypothesis is used, but different evidence is obtained. In the application, this translates to: IF: THEN:

there is a {increasingr decreasing} effect of the {Property} the {Type} {Component} will fail by {Diagnosis-1} or {Diagnosis-2}...

7. Stage II. Propertyr r diagnosis analysis

CERTAINTY FACTOR: CF2 (from Table 7).

It is a manageable and finite task to compile a database containing all possible materials properties. It is a much greater task to compile a database containing actual values of these properties for all available engineering materials, although this has been attempted

Table 10 summarises the results of a knowledge elicitation interview with an expert, in response to the set of questions, as defined by questions derived from Rule 2, for polymeric coatings only. 7.1. Combination of CFs: incrementally acquired e¨ idence

Table 8 Certainty factors from a knowledge elicitation interview with an expert Diagnosis

Certainty factor

d2 d8 d9 d16 d17 d18 d23

0.4 0.8 0.8 y0.4 y0.8 0.2 0.6

Note. di s diagnosis label Žsee Table 4..

To make the selection of material, a judgement could be made based upon the knowledge from Rule 1 or Rule 2 independently. However, a better judgement is possible using the available knowledge from both interviews, Tables 8 and 10, by combination of Rule 1 and Rule 2. Certainty Theory tells us that certainty propagates through similarly concluded rules, i.e. multiple rules can be written to support a hypothesis. In plain language, it is natural for humans to feel more confident about a conclusion when evidence has been obtained from more than one source. The technique

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Table 9 Classification of materials properties Class

Type

Number

Property Žfrom Ashby.

Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Intrinsic Application Application

Physical Physical Physical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Mechanical Thermal Thermal Thermal Thermal Thermal Thermal Thermal Thermal Electrical Electrical Electrical Electrical Wear Chemical

p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27 p28 p29 p30

Atomic volume Žaverage. Energy content Density Hardness Toughness Young’s modulus Shear modulus Yield strength Ductility Endurance limit Tensile strength Compressive strength Poisson’s ratio Bulk modulus Loss coefficient Modulus of rupture Specific heat Thermal expansion Thermal conductivity Glass temperature Melting point Latent heat of fusion Maximum service temperature Minimum service temperature Breakdown potential Dielectric constant Resistivity Power factor Wear resistance Chemical resistance

Application Application Application Application Application

Geometrical Geometrical Fabrication Fabrication Fabrication

p31 p32 p33 p34 p35

Dimensions Shape Porosity Surface roughness Bond strength

Application Application Application Application

Economic Economic Biological Cosmetic

p36 p37 p38 p39

Cost Recycle fraction Biofouling resistance

whereby multiple rules are combined is known as incrementally acquired e¨ idence. When working with CF index values, the rules of combination vary depending upon the signs. Thus: CFcombine Ž CF1 ,CF2 . s CF1 q CF2 ) Ž 1 y CF1 . ; both ) 0 s CF1 q CF2 ) Ž 1 q CF1 . ; both - 0 s Ž CF1 q CF2 . r Ž 1 y min 
Property Žfrom Munger.

Hardness Direct impact resistance Flexibility Flexibility

Thermal expansion

Maximum dry heat

Dielectric constant

Friction, abrasion resistance Resistance to water, chemicals, atmospheric environment Žweathering, ageing, oxidation.

Adhesion, cathodic disbondment

Biofouling resistance Appearance, colour, dirt pick-up

likelihood of the hypothesis, H, and hence to gain more confidence in the computer prediction. 7.2. The application: a polymeric coating for immersion In a generic classification such as that of Ashby w15x it is difficult to use language and definitions that cover all possible applications, so for a particular application it is necessary to adopt slightly different language and make small modifications to the properties considered. In the domain of coatings technology, Munger is an international coatings expert of high renown w22x, therefore, his work was consulted for the application

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Fig. 11. Knowledge tree for failure of paint by wear and erosion.

used here. Munger lists numerous properties which he considers important for selecting the best coating and which, for comparison, are also listed in Table 9. It can be seen at once that, although terms differ slightly, the properties themselves compare directly with the generic properties listed by Ashby. Because of the strong focus on the application, additional properties are suggested, such as the cosmetic property, p39, which is almost always of importance in paints. Some properties acquire new attributes when used in a particular application. Thus, ‘dirt pick-up’ is associated with appearance, but might also be a function of porosity, p33, or of surface roughness, p34. Cathodic disbondment is a property that is generally found only in coatings technology. When the elicitation process is carried out for Rule 2 in the context of the application, the relative importance of each property is evaluated in terms of each possible diagnosis. Filtering eliminates diagnoses and properties that are not relevant in the application. Thus, from the 24 original diagnoses, seven remain after filtering and are listed in the seven columns of Table 10. Only 13 of the total list of 39 properties are relevant and these are the rows of Table 10.

8. Stage III. Selection process through optimisation In a separate work w23x, property values were sought for coatings that would illustrate the KBS function. It is noted that many sources of such data do not quote specific numeric values but deal in ‘uncertain’ quantities. Thus, for example, the hardness of a specific paint might be quoted as ‘very good’ or ‘moderate’, whilst abrasion resistance might be given as ‘fair’. These uncertain terms need to be translated into CF index values that compare with those obtained from the elicitation process. Thus a paint with an ‘excellent’ hardness might be expected to ‘almost certainly not’ fail by the appropriate diagnosis, i.e. CFs y0.8, whilst

a paint with a ‘moderate’ hardness would be considered to ‘maybe not’ fail: CFs y0.4. Table 11 lists an evaluation of CF values for the 13 relevant properties of two possible marine coatings: chlorinated rubber and coal tar epoxy. To decide which coating will give the best performance, the certainty factors are recalculated using the rule of incrementally acquired evidence. Table 12 lists results for two diagnoses, d8 Žabrasive load leading to wearrerosion. and d16 Žtemperature.. These two have been selected for illustrative purposes; the same technique could be used for all diagnoses. The columns marked ’elicit’ are obtained by use of the rule for incrementally acquired evidence, i.e. the CFs for d8 in Tables 8 and 10 have been combined according to the equation given above to give an overall certainty factor for failure by abrasive load. Similarly, the CFs for diagnosis d16 given in Tables 8 and 10 have also been combined using the same equation to give an overall certainty factor for failure by effect of temperature. This resulting CF is then combined with the appropriate value for each coating, in Table 11, so as to give an overall CF for the performance. The data tells us that the coating will almost certainly fail by diagnosis d8 Žhigh positive values. and almost certainly not fail by d16 Žhigh negative values.. It also tells us that in most cases the coal tar epoxy should out-perform the chlorinated rubber paint, except for the property of service temperature where the coal tar epoxy has inferior characteristics. However, the specification of the application excluded temperature as a stimulus, assuming that all environments would be at ambient temperatures. Thus, the computer is clearly able to discriminate between the two coatings for seawater performance and to suggest the coal tar epoxy as the best. Of course, this was something that the human expert already knew, but the KBS should always be used first in situations well understood by humans. 8.1. Closing remarks Humans are naturally sceptical that computers will ever perform in the way that is claimed, and there is no doubt that there is still some way to go in the design of reliable KBSs for engineering applications. Pocket calculators have been wholly accepted into everyday life and humans rely almost exclusively on them for all but the most basic calculations, yet it is not necessary to understand how the microprocessor works to be confident about the results. The fundamental basis of the microprocessor is invisible to the human who only interfaces with the keypad and liquid crystal display. KBSs will be similarly successful when they relate well with humans, showing only the types of details a human will want to see or understand. Essentially, they must meet the criteria of the famous Turing test, i.e. the human should not be able to tell whether the responses

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Table 10 Certainty factors from a knowledge elicitation interview with an expert

Label

Property

Environmental attack d2

p4 p5 p6 p7 p23 p29 p30 p31 p32 p33 p34 p35 p38

Hardness Toughness Young’s modulus Shear modulus Maximum service temperature Wear resistance Environmental resistance Dimensions Shape Porosity Surface roughness Bond strength Biofouling resistance

y0.6 y0.6 y0.6 y0.6 y0.6 y0.6 0.8 y0.8 y0.8 0.2 y0.6 y0.6 0.1

Abrasive load d8

Impact load d9

Temperature

Radiation

Pressure

Biofouling

d16

d17

d18

d23

0.8 0.5 0.5 0.5 y0.6 0.8 y0.6 0.4 0.4 0.5 0.6 0.7 0.0

0.8 0.6 0.6 0.5 y0.6 0.2 y0.6 0.5 0.5 0.5 0.2 0.5 y0.6

y0.8 y0.8 y0.8 y0.8 0.8 y0.8 0.6 0.1 0.1 0.1 y0.6 0.5 0.2

0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0

0.7 0.7 0.6 0.6 y0.8 y0.8 y0.8 0.7 0.7 0.6 y0.6 y0.6 y0.6

y0.8 y0.8 y0.8 y0.8 y0.2 y0.8 y0.2 y0.6 y0.6 0.2 0.2 y0.6 0.8

Note. di s diagnosis label ŽTable 4.; pk s property label ŽTable 9.. Table 11 Certainty factors that a coating will give good performance, as determined by its property, for two different coatings systems Label

Property

Chlorinated rubber

Coal tar expoxy

p4 p5 p6 p7 p23 p29 p30 p31 p32 p33 p34 p35 p38

Hardness Toughness Young’s modulus Shear modulus Maximum service temperature Wear resistance Chemical resistance Dimensions Shape Porosity Surface roughness Bond strength Biofouling resistance

y0.2 y0.2 y0.2 y0.2 y0.8 y0.2 y0.6 y0.4 0.0 0.0 0.0 0.0 0.0

y0.6 y0.6 y0.4 y0.4 0.4 y0.4 y0.8 y0.8 0.0 0.0 0.0 0.0 0.0

were delivered by a computer or another human. They must provide answers at least as good as the human could and must function in such a way as to give the human no cause for concern about the way that they work.

9. Conclusions

Note. Data derived from performance data contained in Renzo w23x.

A generic model of the knowledge structure of materials performance, applicable to both materials selection and failure analysis, has been presented and applied to a defined task to select a polymeric coating for an aggressive marine application. Certainty Theory has been applied to two distinct situations to create two propagating rules. The technique of incrementally acquired evidence has been used to combine the results

Table 12 Certainty factors for coating performance considering two diagnoses Label

p4 p5 p6 p7 p23 p29 p30 p31 p32 p33 p34 p35 p38

Property

Hardness Toughness Young’s modulus Shear modulus Maximum service temperature Wear resistance Chemical resistance Dimensions Shape Porosity Surface roughness Bond strength Biofouling resistance

d8 } Abrasive load

d16 } Temperature

Elicit

Chlorinated rubber

Coal tar epoxy

Elicit

Chlorinated rubber

Coal tar epoxy

0.96 0.92 0.92 0.92 0.00 0.96 0.00 0.84 0.84 0.88 0.92 0.96 0.75

0.95 0.90 0.90 0.90 y0.80 0.95 y0.60 0.73 0.84 0.88 0.92 0.96 0.75

0.90 0.80 0.87 0.87 0.40 0.93 y0.80 0.20 0.84 0.88 0.92 0.96 0.75

y0.88 y0.88 y0.88 y0.88 y0.88 y0.88 0.33 y0.25 y0.25 y0.25 y0.88 0.00 y0.25

y0.90 y0.90 y0.90 y0.90 y0.98 y0.90 y0.40 y0.55 y0.25 y0.25 y0.88 0.00 y0.25

y0.95 y0.95 y0.93 y0.93 y0.80 y0.93 y0.70 y0.85 y0.25 y0.25 y0.88 0.00 y0.25

Note. The columns labelled ‘elicit’ are incrementally acquired CFs from Tables 8 and 10. The columns for the coatings are incrementally acquired CFs using Table 11.

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of two knowledge elicitation interviews, and to make computer predictions of system performance. The results give hope for further use in a broader range of engineering materials situations.

Acknowledgements The authors wish to express thanks to Dr K. Stokes of the Corrosion Group, Structural Materials Centre, Farnborough, for help in the definition of the application described. References w1x Roberge PR, Trethewey KR. A Knowledge-Based Structure to Improve Learning Lessons in the Military. Corrosionr96. NACE International, Denver CO, paper 96640, 1996. w2x Trethewey KR, Roberge PR. Corrosion Management in the 21st century. Br Corr J 1995:192]197. w3x Trethewey KR, Roberge PR. Lifetime prediction in engineering systems. The influence of people. Mater Design 1994;15:275]285. w4x Angwin M, Nelson JL, Syrett B. Technical Database Design. Corrosionr96. Denver CO: NACE International, paper 96366, 1996. w5x Soper PJ, Boardman C, Trethewey K. Using Hypermedia to Modernise Legacy Expert Systems. Corrosionr98. San Diego CA: NACE International, paper 98398, 1998. w6x Pearl A. Sun’s Link Service, A Protocol for Open Linking. In Hypertext ’89. Pittsburg, PA: ACM Press, 1989. w7x Fountain AM, Hau W, Heath I, Davis HC. Microcosm, ‘An Open Hypermedia Environment for Information Integration’. In: Hypertext, Concepts, Systems and Applications. Rizk A, Streitz A, Andre T, Žeditors. Inria, France: Cambridge University Press, 1990. w8x Roberge PR, Trethewey KR. An Indexing System of Corrosion Failures for Case-Based Reasoning. Corrosionr96. Denver CO: NACE International, paper 96359, 1996.

w9x Graham-Jones, PJ, Mellor BG. The Development of a Generic Failure-Analysis Expert System Based on Case-Based Reasoning. Corrosionr96. Denver CO: NACE International, paper 96372, 1996. w10x Sturrock CP, Bogaerts WF. Computer Learning Systems in Corrosion. Corrosionr96. Denver CO: NACE International, paper 96657, 1996. w11x Sturrock CP, Bogaerts WF. Classification and Prediction of Corrosion Phenomena via Cluster Analysis. Corrosionr96. Denver CO: NACE International, paper 96383, 1996. w12x Durkin J. Expert Systems, Design and Development. New York, NY: Macmillan, 1994. w13x Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975;23:351]379. w14x Watson M et al. Cambridge Materials Selector. Cambridge UK: Granta Design, 1994. w15x Ashby MF. Materials selection in mechanical design. London, UK: Pergamon Press, 1992. w16x Bea RG. Marine Structural Integrity Programs. Report to Ship Structure Committee. Washington, DC: US Coast Guard, 1992. w17x Hoar TP. Report of the Committee on Corrosion and Protection. London, UK: HMSO, 1971. w18x T-3U-4 Working Group. Standard Format for Computerized Electrochemical Polarization Curve Data Files. RP 0197-97. Houston TX: NACE International, 1995. w19x Trethewey KR, Roberge PR. A Model of Corrosion Expertise. Corrosionr96. Denver CO: NACE International, paper 96360, 1996. w20x ASTM G01.03 Working Group. Standard Guide for Formats for Collection and Compilation of Corrosion Data for Metals for Computerized Database Input. West Conshohocken PA: ASTM, 1995. w21x Trethewey KR, Roberge PR. Modelling Human Interventions in Corrosion Failures. Corrosionr97. New Orleans, LA: NACE International, paper 97325, 1997. w22x Munger C. Corrosion Prevention by Protective Coatings. Houston TX: NACE International, 1984. w23x Renzo DJ, editor. Handbook of Corrosion Resistant Coatings. Park Ridge NJ: Noyes Data Corporation, 1986.