Fabricating Composite Materials-A Comprehensive Problem-Solving Architecture Based on Generic Tasks

1. INTRODUCTION

The emerging field of composite materials offers one pivotal area for the establishment of a

revitalized American industrial base. There is a key enabling step for realizing this potential in

which artificial intelligence (AI) may prove to be important: enabling a rapid “specifications to

manufacturing” time; i.e. shortening the time between setting material specifications, and suc-

cessful commercial manufacturing of a material meeting those specifications.

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Fabricating Composite Materials:A Comprehensive Problem Solving Architecture

Based on a Generic Task Viewpoint

Jon Sticklen & Ahmed Kamel Martin Hawley & V. AdegbiteAI/KBS Lab–CPS Dept Composite Materials and Structures Center

Michigan State University Division of Engineering ResearchEast Lansing, MI 48824 Michigan State University

[email protected] East Lansing, MI 48824

AbstractFabrication of advanced composite materials will be a pivotal industrial activity inthe twenty first century. Over the last three years we have conducted collaborative re-search aimed at applying artificial intelligence techniques to selected design and fab-rication problems in the composites materials area. More particularly, we have exer-cised and extended techniques drawn from the Generic Task framework for task spe-cific architectures to produce problem solving frameworks for solving (a) design ofthermoset/thermoplastic epoxy-resin materials, and (b) fabrication monitoring andcontrol for advanced microwave curing of thin section epoxy-resin materials. Morerecently, we have developed a comprehensive PS architecture for the “composite ma-terials design and fabrication lifecycle.” In this report, we first describe our newly de-veloped PS architecture for the composites design/fabrication lifecycle. We then goon to illustrate two of the components of this architecture by describing our two run-ning prototype systems, both of which have produced encouraging results. We con-clude with a discussion of related work in other laboratories, and future work beingplanned in our laboratories.This paper is both a position paper on a proposed problemsolving architecture for a significant industrial problem, and a report on two workingcomputer system which are implementations of components of the proposed architec-ture.

In our collaborative research, we have concentrated on the most prevalent subfield of com-

posites: polymer composite materials, although we believe that most of our experience is trans-

ferable to the entire area of composites, and perhaps beyond. Polymer composite materials can

be viewed as a modern generalization of metallurgy. Like metallurgy, the ultimate goal in

composites is to understand properties of physical materials and methods of material

fabrication which enable the creation of materials with desired properties. Metallurgy deals

with properties and fabrication of materials made from metals; polymer composites deals

chiefly with properties and fabrication of materials created largely from the realms of polymer

science and chemical engineering; the area is distinctly multidisciplinary. Another difference

between metallurgy and composite materials in general is the basis for the term “composites.”

In metallurgy, typically either metals are reacted chemically to form a new compound (as in

steel) or metals are commingled at the molecular level (as in metallic alloy formation such as

tin). In composite materials, the processed material typically will retain macroscopic areas re-

sembling the starting materials. For example, in epoxy-resin carbon fiber composite materials,

the carbon reinforcing fiber retains much of its individual identity after processing is

completed.1

A typical chronology for fabricating a new composite material is as follows [1]. First, macro-

scopic properties which are desired in the completed composite are set by consideration of the

application to which the composite part will be put, the geometrical properties (e.g. thickness)

of the finished part, cost constraints, and so on. Properties such as final material tensile

modulus, resistance to acids and alkalis, electrical resistance are parametrized in this initial

step. Based on these desired properties, the composite designer proposes an initial plan for the

production of the composite. This plan includes both an ingredients list for all materials to be

initially present, and a preliminary plan which states how the initial mixture is to be processed.

1. Metal-matrix composites also exist, but the ideas expressed here are the same.

IEEE Expert, Special Track — Sticklen, Kamel, Hawley, Adegbite

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Next, the composite designer estimates how well the proposed composite design meets the ini-

tially stated, desired properties. This determination is typically carried out by actually pro-

ducing samples of the composite, then performing laboratory testing to determine properties of

interest. Following one round of design proposing, and matching to specifications, successive

rounds of redesign and laboratory testing are usually required before convergence of proposed

composite properties to desired properties takes place. This fabrication life cycle is depicted in

Figure 1, without any of the iterative design/test loops

shown.

There are two broad ways in which AI problem solving

techniques might be leveraged to shorten the design and

fabrication life cycle shown in Figure 1 for new compos-

ites. One AI contribution could be to produce initial plans

for fabrication that require less iteration before an accept-

able product is produced. The most time consuming parts

of the above cycle are the physical fabrication and laborato-

ry test steps.

A second potential AI contribution could be to provide

more flexible fabrication methods. Currently, commercial

composites fabrication is carried out by operators who

monitor sensed values (temperature, dielectric constant,

temperature gradients...) in autoclave devices and make

adjustments in curing parameters (pressure, temperature, electromagnetic [EM field],...) in the

autoclave to keep the curing process “on track.” In AI terms, we could characterize this activity

as process diagnosis followed by reactive planning. Development of reactive planning tech-


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Figure 1: Composite MaterialFabrication Life Cycle

Designthe Fabrication

Plan

Applythe Fabrication

Plan

specification of macroscopicproperties for composite

static fabrication plan

applicationcharacteristics

Select theComposite Properties

1

2

3

niques appropriate for the composite material fabrication domain would lessen the need to have

near optimal initial fabrication plans, and hence shorten substantially the typical fabrication

chronology outlined above. Because of the near impossibility of anticipating everything that

might happen during a specific curing operation, developing a real time, reactive planning ca-

pability for control of the composite curing process would make the initial design of the plan

much simpler. Moreover, by being able to react appropriately to each individual curing in-

stance, the final composite product should also be of more uniform, higher quality.

We have developed approaches for the second and third information processing tasks shown

in Figure 1: Design the Fabrication Protocol, and Apply the Fabrication Protocol. Our analyses

are based on the Generic Task viewpoint [2; 3; 4; 5], where we use the term “information process-

ing task” in Marr’s sense [6; 7]. Each of the boxes of Figure 1 represents a subtask which should

be solved, and for each, there is a specified input and output. The boxes thus represent problem

solving mappings which must be accomplished.

Below we first describe our sub-architecture for designing the initial fabrication plan (Box

2, Figure 1) - which results in what we term the “static design” - and an operational computer

system that carries out the task of producing a static design. Next we describe our sub-architec-

ture for fabrication monitoring and control (Box 3, Figure 1), and a currently operating com-

puter system we have built which carries out this task. We conclude with a discussion of relat-

ed work, and a description of our future research goals and plans.

It is important to understand that this report is both a position paper suggesting a compre-

hensive problem solving architecture for a significant industrial problem, and a report of two

proof of principle, operational computer systems which were conceived in the framework of

the proposed comprehensive architecture.


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2. DESIGNING STATIC FABRICATIONPLANS

At the task analysis level of description, the stat-

ic design of fabrication plans (Box 2, Figure 1) is

shown in Figure 2. Types of knowledge that can be

utilized in the input-output mapping of Figure 2 in-

clude, as indicted, compiled level knowledge of

how to build fabrication plans, and model-based

knowledge of composite materials. This mirrors the two major ways in which composites are

designed currently. In industrial settings, composite materials are typically designed by refer-

ence to prior experience; in research laboratories composite design is typically driven by more

fundamental understanding of how composite materials function.2

Because the area of composite materials is

relatively new, compiled expertise is not

always available, and when available is typi-

cally incomplete. Thus, an experience-based

problem solver cannot by itself be assured of

producing reliable results. The reason is fa-

miliar - the compiled level problem solver

may encounter novel design requirements.

The knowledge for the compiled level system

we describe below would be largely obtained

2. We would argue that the research activities in composites is driven reasoning over a model that helps to pin-

point where knowledge is lacking.


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Figure 2: Task Description forDesigning the Fabrication Plan

Figure 3: Interacting Routine Designer andFunctional Reasoner

RoutineDesigner

FunctionalReasoner

Task: with statedconstraints,parametrize adesign template

Task: with given startingconditions, deriveconsequences ondevice

• • • • • •

• • • • • •

proposedchange

result ofproposedchange

Design the Fabrication Protocol

input: specifications for macroscopic properties of the target composite material

possible types of useful knowledge:a) compiled knowledge giving

recipe-like protocolsb) knowledge of how composite

materials “work”

output: a fabrication protocol (both materials andprocesses)

from industrial designers. Another rich source of knowledge about composite materials which

is used in design is model-based knowledge. We intend to eventually utilize both sources.

Our complete architecture for the static design box of Figure 1 includes a model-based rea-

soning component - as shown in Figure 3. We will utilize another Generic Task for this exten-

sion: Functional Reasoning [8; 9; 10]. From an AI viewpoint, the expected integration of Routine

Design and Functional Reasoning will be similar to that already performed by Sticklen and

Chandrasekaran for diagnostic problem solving [11; 12]. The analogy to the previous research

will be that the compiled level unit (Routine Design) will focus problem solving of the deep

level unit (Functional Reasoner) in the same sense that compiled level classification problem

solving played the role of focuser for functional reasoning previously. The proposed interaction

between the existing Routine Design system and the Functional Reasoning system that is in

progress is graphically shown in Figure 3. The “proposed change” in this diagram refers to the

setting of a design parameter by the compiled level Routine Design system. The “result of pro-

posed change” refers to the consequences which will result from that design decision. These

consequences will be used (a) as a check on the evolving design, and/or (b) as an aid for the

Routine Design unit in making the next parameter fixing decision.

Our system building attention has focused on developing the compiled level component of

the overall system. Below we describe the Routine Design-based system we have developed.

In the remainder of this section, we deal solely with the compiled level component of Figure 3

- the component which we have implemented.

2.1. CAPTURING COMPILED PROCESS PLANNING USING ROUTINE DESIGN

One of our initial projects to fill in the problem solving pieces for the overall architecture


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described above aims at producing composite material fabrication plans based on prior experi-

ence. This problem solver is backed by well known theory of Routine Design [13], and has

been implemented in DSPL [14], the task specific language for Routine Design systems. Our

use of Routine Design in this application parallels that by Chandrasekaran et al, who previous-

ly implemented a planning system utilizing DSPL [15].

Space here limits us to a short description of the features of Routine Design; the reader

should consult [16] for a complete discussion. The general structure of a Routine Design prob-

lem solver is shown in Figure 4. A Routine Design problem solver consists of a collection of

design specialists. Each specialist is responsible for contributing a small part of the overall de-

sign. A part of the decision making carried by each specialist is to determine (locally) which of

a number of plans to carry out. S1 in Figure 4 has two such plans to choose between. Generally

each specialist chooses just one of its plans. The actions that constitute a plan could include

doing a calculation for a local value, satisfying a local constraint, or requesting another special-

ist to elaborate the current plan. For example, the left plan in S1 invokes the S2 specialist. If a


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Figure 4: General Structure of a design specialist

Specialist S1

Plan

Planning DecisionConstraintCall Specialist S2

Plan

Planning DecisionConstraintCall Specialist S2, S3

• • •

Specialist S2

Plan

Planning DecisionConstraint

Plan


• • •

Specialist S3

Plan


Plan


• • •

plan fails then alternate plans are tried. If part of a plan fails then an attempt is made to rede-

sign the part of the plan that caused the failure. Potential causes of failure (i.e., where to try to

fix a plan) are precompiled into the specialist.

Chandrasekaran et al have previously attacked a planning problem from a Routine Design

viewpoint [15]. Their experi-

ence was in the domain of

mission planning for Air

Force offensive counter-air

strikes. The prior work dem-

onstrated that the framework

of Routine Design eases the

conceptual burden of produc-

ing a plan for an often solved

problem by decomposing the

overall plan into a number of

more easily handled substeps. Our research described in the section below builds on that early

work.

2.2. A ROUTINE DESIGN SYSTEM FOR DESIGNING FABRICATION PLANS FOR COMPOSITE MATERIALS

Figure 5 shows the Routine Design decomposition for a part of the task of designing a thin

film, epoxy-resin composite with fiber reinforcement. This decomposition includes both mate-

rial selection, and the selection of fabrication plans.

The top level specialist, CompositeMaterial, has one plan which first calls on the Matrix

specialist to select a suitable matrix to achieve the required properties. It then calls on the


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Figure 5: DSPL Specialist Hierarchy for Designing Fabrication Protocols

for Composite Materials

Composite Material

Matrix

Epoxy

Amine Dicyandiamide

AliphaticAmine AromaticAmine

Fiber

CarbonFiber GlassFiber

CureConditions

Curing Agent

Anhydrides

Fiber specialist to select an appropriate fiber. Eventually, it calls on CureConditions to design

the appropriate cure conditions given the chosen materials.

The Matrix specialist has one plan. This plan first calls on the Epoxy specialist to select an

appropriate type of epoxy.3 The Matrix specialist then calls on the CuringAgent specialist to

select a suitable curing agent.

The CuringAgent specialist has three alternative plans from which to choose. Based on the

properties required for the composite material being designed (mainly the required usage and

the thermal properties represented in the glass transition temperature) this specialist chooses

one of its three plans. This plan in turn calls on the Amine specialist, the Anhydrides special-

ist or the Dicyandiamides specialist in order to select the appropriate material to be used as the

curing agent. The Amine specialist in turn has two alternative plans from which to choose

according to the desired usage of the final product. These plans in turn call on either of the Ar-

omaticAmines specialist or the AliphaticAmines specialist.

The Fiber specialist similarly has two design plans to choose from. This choice is done

based on the required tensile properties represented in the tensile modulus of the required

product. These two plans in turn select either the CarbonFiber specialist or the GlassFiber

specialist. These specialists are responsible for selecting the appropriate type of carbon, or

glass fiber respectively. Finally, the CureConditions specialist has one plan that is responsible

for setting the appropriate cure conditions (temperature, pressure, and time) based on the

selected materials.

2.3. SAMPLE PROBLEM

Consider a design in which we desire a composite material that has a glass transition

3. Currently there is only one choice in our system: diglycidyl ether of bisphenol A (DGEBA).


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temperature of at least 200°C and a tensile modulus of 50 GPa., and which will be used in an

outdoor structure application.4

Referring to Figure 5, our system starts by calling the top level specialist, CompositeMate-

rial. This specialist calls on the Matrix specialist, which in turn calls on the Epoxy specialist.

Epoxy chooses DGEBA. The Matrix specialist then calls on the CuringAgent specialist.

CuringAgent has embedded knowledge that amines are a preferable choice for civil engineer-

ing applications (e.g., outdoor structures), and hence will make the appropriate selections. The

Amines specialist now examines the requirements and finds that aromatic amines are more

suited for civil engineering applications. Now AromaticAmines is called upon and uses the

required glass transition temperature to select diaminodiphenylsulfone (DDS) which has a

glass transition temperature of 220°C.

The CompositeMaterial specialist then calls on the Fiber specialist to select the most

appropriate fiber. This specialist examines the required tensile modulus and because it is not a

high value, it selects glass fiber as appropriate. Next, GlassFiber chooses E-glass as the type

of fiber to be used because it has a tensile modulus of 52 GPa. The CompositeMaterial spe-

cialist will then call on the CureConditions specialist which inspects the materials selected to

this point, and selects a cure cycle of 1 hour at 150°C followed by 3 hours at 220°C at

atmospheric pressure. This concludes the design process for the fabrication plan given the

input specifications.

If the required tensile modulus were set to 100 GPa. (instead of 50 GPa) the design process

would take the same actions as before until it comes to the Fiber specialist to select a fiber. At

this stage, the Glass design plan is ruled out because of the higher tensile modulus. The Car-

4. In this interim system we are partially combining the problem solving of the first two modules of Figure 1.

We have given inputs to our problem solver that include both macroscopic properties as would be input to the

second module, and the identification of the application domain as would be input to the first module.


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bonFiber specialist is then called on, which chooses AS4-Carbon because it has a tensile

modulus of 145 GPa.

As another variation, we could relax the requirement on the glass transition temperature to

be 150°C ( instead of 200°C). In this case, the AromaticAmine specialist now chooses meta-

phenylendiamine (MPDA) which has a glass transition temperature of 160°C. This choice is

based on a precompiled knowledge that MPDA is the preferred choice of aromatic amines. The

real-life reason for this choice is that MPDA has a relatively low cost compared to DDS which

has a higher glass transition temperature and which was chosen in the first case. This choice of

another curing agent further affects the decision made by the CureConditions specialist which

selects a cure cycle of 2 hours at 125°C followed by 2 hours at 175°C at atmospheric pressure.

2.4. DISCUSSION OF ROUTINE DESIGN SUB-COMPONENT

The testbed system described above is a functioning computer system, and is our initial step

in implementing a problem solving architecture for the static design of fabrication plans for

composite materials. The example we discussed is straightforward from a composite materials

viewpoint. However, the straightforward nature of the problem solution is facilitated by the

Routine Design framework in which we developed our application. Routine Design has not

been previously utilizied as a framework for raw materials selection and process planning for a

fabrication process.

Our current system has been tested over a number of sample problems proposed by compos-

ite materials experts from the Composite Materials and Structures Center, Michigan State Uni-


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versity, and has produced reasonable results according to the composites experts. Although

these results are encouraging, the current coverage of the DSPL problem solver is narrow in re-

lation to the total domain of epoxy-resin composite materials. On theoretical grounds, and on

the basis of results from other domains, we can argue that our selection of Routine De-

sign/DSPL will scale well to handling greater domain coverage.

We are in the process of broadening the coverage of the current DSPL implemented plan-

ning system. Because of the modular nature of a DSPL representation, additional compiled

knowledge will be straightforward to add by non-AI researchers. The additional domain cover-

age will include a greater range of epoxies and fiber types initially, and will eventually branch

out to include interphase relations (particularly fiber treatment conditions), and to include dif-

ferent matrix materials, in particular ceramic and metal matrix materials.

2.5. DISCUSSION OF ENTIRE STATIC DESGIN COMPONENT

The Routine Design approach described above is a part of our problem solving architecture

to produce a static design, which in turn is a part of our complete architecture. Routine Design

is a typical experience-based, compiled-level knowledge based approach. Like other compiled

problem solving techniques, Routine Design by itself will fail in the face of a novel situation.

As noted in the introduction to this section, our complete problem solving architecture for stat-

ic design production will include a model-based reasoning component. This component will

have a single task: checking the applicabilty of compiled level decisions. As the Routine De-

sign problem solver systematically makes design decisions (as illustrated above), a Functional

Reasoner will use model-level understanding about the composite materials domain to check

the impact of the each decision for the particular design problem at hand.


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We do not currently have a working prototype for the model-based reasoning component,

but are now in the design phase for this piece of the architecture. Because our complete archi-

tecture for static design depends strongly on the compiled-level (DSPL) component being typi-

cally correct in its design decisions (if it were typcically incorrect, then there would be no point

in including it in the architecture), our efforts to date have been expended in developing the

proof-of-principle application showing that a Routine Design framework can be fruitfully ap-

plied in the area of raw materials selection and process planning. Having demonstrated that, we

are now in position to develop a full proof-of-principle application for static design, including

the model-based reasoning component.

3. APPLYING THE FABRICATION PLAN

In the previous section we first discussed our architecture for producing the static plan for

fabricating a composite material. We then described our pilot project in this area to utilize

Routine Design to implement the compiled problem solving component of the static design

unit. The output of both our current problem solver (and the augmented one under develop-

ment) is a static fabrication plan for producing a composite material given initial specifications.

Developing this static fabrication plan does not complete the job. There are two reasons.

First, because the area of composite materials is relatively new, knowledge gaps exist in what

practitioners are able to do without performing hands on fabrication. Second, even if static de-

sign of fabrication plans were perfect, still the curing process might proceed in unpredictable

ways because of materials nonuniformity, slight flaws in curing devices, and so on. These two

problems force monitoring the composite material curing process, and in fact, the two areas are

reflected in two different types of monitoring and process diagnosis/evalutaiton. The first level

expansion of the “Apply the Fabrication Protocols” task from Figure 1 is depicted in Figure 6.


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Monitoring is the first step in

the application of the static plan.

This monitoring step involves

two substeps: sensing and pro-

cess diagnosis. The sensing sub-

step is the continuous sampling

of important state variables in

situ in the device being used to

cure the composite material. The

process diagnosis substep is a

step of data abstraction and clas-

sification conceptually similar to

Clencey’s heuristic classification

model [17]. Note that this process

diagnostic step plays the critical

role of characterizing the current

state of the composite curing

process in terms of high level ab-

stractions which then will form

the basis for problem solving to

control the curing process.

Once a characterization of the current state is available, then there are two types of problems

that might become apparent: the entire plan for undertaking the fabrication may need alter-

ation, or the cure conditions may be slightly off track and need to be adjusted. The ability to

detect problems in the total fabrication plan based on the way in which the curing process is


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Figure 6: Further Decomposition for Applying the Fabrication Protocols Task

static fabrication protocols

sensecuring

environment

interpret(diagnose)

Apply the Fabrication Protocols

Monitoring

values of state variables

characterization of curing situation

refinestatic fabrication

protocols

altered protocols

maintainstatic fabrication

protocols

control actionsto take

Global Replanning Local Reactive Planning

controlcuring

environment

• • •

progressing is lodged in the module labeled “Global Replanning” in Figure 6. The goal of this

information processing task is to produce a revised fabrication plan from the point at which the

fabrication has currently reached. For example, an acceptable output from the Global Replan-

ning step would not include a change of materials selection5, but it could include a change in

the temperature/pressure regimes to be applied at stages of the fabrication process downstream

from the current point.

In general, Global Replanning is a formidable problem solving task. In synopsis, Global Re-

planning addresses the issue of knowledge gaps in understanding composite materials. If we

produced a perfect fabrication plan based on current understanding of composites, that fabrica-

tion plan would none the less not always succeed. The manner in which the fabrication plan be-

gins to fail during fabrication should shed light on how our knowledge was faulty, and on what

we might do because the fabrication plan is failing in a given manner. We would use this new

knowledge to alter the remainder of the fabrication plan to still produce a composite material

meeting the desired specifications.6

While the goal of Global Replanning is to modify the fabrication plans, the goal of Local

Reactive Planning is to alter the current conditions so as to maintain desired curing conditions

according to the fabrication plan. Local Reactive Planning addresses the problems of material

nonuniformity, variations in cure equipment, and so on. The existence of a problem solving

that could do Local Reactive Planning (in the sense of Figure 6) would free other problem

solving modules of the burden to constantly “keep the fabrication on track.” Notice that the

output from the Local Reactive Planning problem solver is a set of control actions to take.

Taking the entire box of Figure 6, the top level story is (a) sense the environment, (b) reason

5. Once the curing process is started, a suggesting a change in materials is tantamount to throwing out the cur-

rent batch of materials and starting over.

6. Clearly, this goal is not always achievable.


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about the sensor values to decide how to react, and (c) control the environment. As was the

case for the static fabrication plan designer described in the previous section, we do not have a

complete implementation as yet for units of Figure 6, and in particular, we have not addressed

at all the problem solving of the Global Replanning unit. But we have implemented a pilot sys-

tem for the Local Reactive Planning subsystem. In the rest of this section, we will describe that

pilot project.

3.1. INTRODUCTION TO COMPOSITE MATERIAL CURING BY MICROWAVE HEATING

Composite material curing is a chemical crosslinking reaction which amounts to heat-activa-

tion of liquid resins to form molecular chains which are interconnected with chemical bonds.

The heat-activation can be achieved by either microwave heating or by conventional thermal

heating. Although thermal heating in an autoclave is much more wide spread, microwave heat-

ing offers a number of advantages including (a) fast direct and outward heating, (b) selective

heating dependent upon the magnitude of loss factor of the constituents in materials, and (c) in-

creased control of material temperature/time profile and input power cycles to optimize the re-

action process [18].

During microwave heating the chemical and or physical properties of the composite changes

as a result of its interaction with the electromagnetic energy. These changes result in a

nonlinear variable equivalent material admittance, and usually mismatched inefficient heating

such as thermal runaway, hot spots within the material, and even reduced material heating. To

efficiently transfer energy to the composite and compensate for complex material load

impedance, the cavity is continuously tuned during processing by adjusting the emitting probe

position and cavity length to match the material-loaded cavity input resistance to that of the


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characteristic impedance of the feed transmission line.

It is this mode tuning or impedance matching activity during curing that is our problem

solving focus. The reader should keep in mind that the terms “mode present” and “mode ab-

sent” refer to the presence or absence of an electromagnetic standing wave within the micro-

wave curing cavity. It is the presence of this standing wave which “heats” the protocomposite

causing the chemical reactions which transform it into the finished composite, i.e., cure the

composite.

We have utilized Functional Reasoning (FR) in our analysis and implementation for Local

Reactive Planning. For a full description of both representation and reasoning from a function-

al perspective, the reader should consult [8; 9; 11].

3.2. SYNOPSIS OF FUNCTIONAL REPRESENTATION

To represent a device from an FR perspective, first the device is recursively decomposed

into its constituent components. In engineered devices, this decomposition typically parallels

major structural systems of the device.

The second step in representing a device functionally is to enumerate the functions of each

component. A function is made up of three elements:

• a Provided clause which states the conditions under which the function will be

applicable. This amounts to a precondition for the function.

• a ToMake clause which states the result which will be achieved after the func-

tion completes. The ToMake clause may be thought of as a postcondition.

• a By clause which points to the causal description of how the function is im-

plemented. We have so far limited our functional representations to implement


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functions by behaviors which are described below.

To complete the functional representation of a device, the implementing behaviors which

are pointed to from functions must be described. Behaviors are directed graph structures in

which the start nodes of the graph are predicates on state variables of the device, and other

nodes are descriptions of changes in state variables. At this level, behaviors resemble frag-

ments of causal nets. However, unlike causal nets, the edges of the directed graph are annota-

tions which indicate a fuller description of why each node transition takes place. These annota-

tions are either pointers to “world knowledge” or to other parts of the functional description it-

self - i.e., to other functions or behaviors.

3.3. THE PROBLEM ADDRESSED

The domain of this model is the micro-

wave curing device which typically has four

basic components: (1) a tunable cavity, (2)

an external microwave circuit, (3) a tempera-

ture sensing system, and (4) material load.

The tunable cavity is shown in Figure 7.

The cavity is a cylindrical brass cylinder

with an internal transverse brass shorting

plates. One of the shorting plates is adjustable to provide varying cavity lengths from 4.5 cm -

22 cm. The material load rests on the other short plate which is fixed in place during process-

ing and is removable for material loading. A semirigid 50Ω impedance copper coaxial probe

serves as a field excitation probe (coupling probe) to couple microwave power into the cavity.


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Figure 7: Schematic view of microwavecuring cavity for composite materials

Lp

Lc

microwavesource

modeadjustment

Adjustments of the cavity length Lc and coupling probe length Lp are currently made by manu-

al rotation of attached knobs and measured within 0.1mm by micrometer indicators.

A rectangular brass block is soldered on the outside of the cavity wall perpendicular to the

direction of the coupling probe. Holes are drilled through this block and the cavity wall for

electromagnetic field sensing and characterization.

A typical curing procedure includes the initial theoretical mode selection based on cavity

physical properties and known processing frequency from theoretical mode charts calculations

[19]. The mode chart provides the theoretical cavity length at which a resonant frequency can

be achieved for an empty cavity. As the cavity is loaded with the material the deviation from

the theoretical cavity length and the loaded cavity length is compensated for by adjusting the

probe length and/or cavity length around the theoretical length to determine the actual process-

ing cavity length.

The tuning activity involves the adjustment of the cavity and probe lengths until the mea-

sured reflected power is minimized. The adjustment is typically a trial and error operation,

where the cavity length is the major adjusting component. For example, a component is adjust-

ed in one direction and then the efficacy of the adjustment is observed on the minimization of

the reflected power. If the effect is not favorable then the direction of adjustment is reversed.

The order of adjustment is typically the cavity length first followed by the probe length.

During curing, changes in the material (prepreg) chemical properties influence the imped-

ance and mode setting of the system. Consequently, the cavity is continuously tuned during the

cure cycle. The effectiveness of the cavity tuning is related to the efficiency of the energy

coupling into the cavity or uniformity of material heating which is related to the quality of the

finished composite part. Thus, the tuning activity is a vital part of the microwave curing pro-

cess.


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The specific problem we have set in this project is to automate the cavity tuning for micro-

wave curing of composite materials. For microwave curing, this problem amounts to a sub-

problem of the Local Reactive Planning problem solver of Figure 6. For exploratory purposes

in this implementation, we have implemented the sensing and control units of Figure 6 with

program “stubs” - that is, the system described below is not currently connected to a real mi-

crowave curing device. However, that connection will be made in the next phase of our re-

search.

3.4. PROBLEM DECOMPOSITION ANDREPRESENTATION

A component decomposition for the mi-

crowave curing process for composite mate-

rials is shown in Figure 8. Most of the ob-

jects in this decomposition are easily under-

stood, at least at the level needed in this discussion. The Cavity is the microwave curing cavi-

ty, which in this representation has components CouplingProbe and ShortPlate. The Prepreg

is the protomaterial which is placed in the cavity to be cured; i.e., the material which will be-

come the composite following success-

ful curing.

The device-function-behavior de-

composition for the top level “device,”

CuringProcess, is shown in Figure 9.7

7. This FR representation means that the device (CuringDevice) has two functions (MakeComposite and Create-

Composite). Going further, the function MakeComposite is “implemented” in three possible ways by the

three behaviors under MakeComposite in the graph.


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Figure 8: Microwave Curing Device

Figure 9: device - function - behaviorfor CuringDevice

CuringProcess

CreateComposite transform-prepreg-to-composite

MakeComposite cure-composite-mode-absentcure-composite-varying-mode

cure-composite-mode-present

PowerSource

CuringProcessCavity

Prepreg

Composite

CouplingProbe

ShortPlate

Previously, we have utilized the FR formalism to represent knowledge about devices. In the

current situation, we are using the FR approach as a process description language, and in effect

representing “how to” knowl-

edge. We have learned from

our experience that there is no

fundamental impact on our

basic methodology.

Let us focus on the behavior

“cure-composite-mode-absent”

in Figure 9. The representation

of this behavior is shown in

Figure 10. Basically, this is a

top level description of the causal chain of events which occur in the microwave curing process

to turn the “prepreg” into a finished composite material is there is no mode present in the cur-

ing cavity; i.e., if there is currently no electromagnetic (EM) standing wave set up that can

properly heat the prepreg.

For purposes here, the first link of the graph of Figure 10 is the most interesting. This link is

a pointer to a lower level function that describes how the cavity should be tuned in case there is

no curing mode present.

The function pointed to, FocusPower, has a number of implementing behaviors, one of

which is shown in Figure 11. It is at the level of Figure 11 that our use of the functional repre-

sentation language as a process control language becomes more clear. We have augmented the

representational primitives of the language by supplying two new types of behaviors: sensing

behaviors and control behaviors. These two additions allow the functional representation to di-


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Figure 10: The “cure-compostite-mode-absent” behavior

mode absent?

set mode to PRESENT

increase temperature

increase temperature

... by function FocusPower of Cavity

... by behavior IncreaseTemperature of Prepreg

... by behavior StartCrosslinking of Prepreg

... by behavior Crosslink of Prepreg

destroy Prepreg

create Composite

rectly interface with a physical device such as the microwave curing apparatus.

Reading the behavior of Figure 11 from the top down, there are first three tests which must

be true if the behavior is to be applicable: the prepreg must be present (i.e., the composite is not

totally fabricated yet, there must be no standing EM wave yet (i.e., no mode), and a control

variable indicating for the interaction between this mode tuning behavior and two others must

be set ABSENT. The first link of the behavior in Figure 11 is a “control behavior” link - it in-

dicates that the next state in the behavior (which increases the cavity length) is achieved by

sending control signals to some external device. As in all FR representations, more detail about

how this state change is achieved can be obtained by examining the behavior pointed to, in this

case a behavior of the short plate of the curing device. Following the increase in cavity length

state change, the behavior of Figure 11 shows a sensing behavior to sample the reflected power


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Figure 11: One of the implementing behaviors of the FocusPower function

prepreg present? mode absent? mode-trial1 absent?

... by control behavior IncreaseCavityLength of ShortPlate

increase cavity-length by 1

... by sensing behavior SenseReflectedPower of Cavity

set reflected-power to amount sensed

... by control behavior IncreaseProbeLength of CouplingProbe

increase coupling-probe by 1

... by sensing behavior SenseReflectedPower of Cavity

set reflected-power to amount sensed

by sensing behavior ModeTuningCheck of Cavity

set mode

... by definition mode-tuning

set mode-trial1to Present

set mode-trial2to Absent

within the cavity. The remainder of the behavior of Figure 11 is straightforward, keeping in

mind that mode-trial-1 and mode-trial-2 are two programming control flags which are used to

cycle between three power focusing behaviors, one of which is that in Figure 11.

We consider the use of programming control flags such as those in Figure 11 to be ad hoc

at present; the practice does not clearly fit into our backing theory of functional representation.

It is clear that to utilize the FR formalism in the domain of process sensing and control, exten-

sions to FR will be necessary. At this point, we are content to build our experience with the use

of the programming flags, and at some later point to reconsider the principled integration of

this construct into FR.

3.5. DISCUSSION OF LOCAL REACTIVE PLANNING

Utilizing a functional approach to capture process sensing and control in the mode tuning

activity for the microwave curing process for composite materials has yielded positive initial

results. These results have been in two areas. First, the simulation runs of the system have pro-

duced “reasonable” control activity in the estimation of composite materials experts. Second,

and at this point more important to us, the composite materials professionals have found FR to

be a “natural” medium for expressing knowledge about the curing device, and about how to

control that device.

The dimension of “naturalness” is of course very slippery. In the final analysis, the utiliza-

tion of FR in the process sensing and control domain will hinge on positive results from actual

experiments in the domain. Currently, as pointed out earlier, the parts of the representation

which either sense external state, or control external devices are implemented by programming

stubs - the current FR-represented system does not control anything in the real world at this


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point. However, we are currently implementing the communications devices necessary to make

the connections between our program and the actual microwave curing apparatus. Once these

communication capabilities are in place, we will proceed with the experiments necessary to test

our approach in a realistic setting.

A comment on the relation between the implemented system for microwave mode tuning,

and the architecture of Figure 6 is necessary. Note again from Figure 6 that the Local Reactive

Planning module, which the mode tuning system is a part of, does not have responsibility for

sensing, although it does have control responsibility. In our implementation of mode tuning,

we have folded in both sensing and control. We made this departure for two reasons. First, we

have not yet begun construction of the sensing and interpretation (diagnosis) module. Second,

the sensed values we need to drive the mode tuning activity are particularly easily obtained and

do not require sophisticated reasoning activities. That is, the values needed in mode tuning are

directly available from sensor input. In general, such direct availability of state variable values

will not be feasible.

Finally, it is important to underscore that the microwave sensing and control system de-

scribed above is an operational computer system. Initially, the links between the computer pro-

gram and the physical microwave curing device were virtual and implemented via program

stubs. The functional reasoning process control module described above is implimented in a

HLL for model-based reasoning (FR/FM), which is currently running on PROCYON CL on a

Macintosh IIci. In work just completed (July, 1991), we have interfaced the functional reason-

ing module with the controlling stepping motors of the microwave applicator device shown in

Figure 7 by using an Omega WBMA12 board resident on the Macintosh. Work in progress will

similarly interface our program directly to the


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4. CONCLUSION

In this report, we have discussed collaborative research in progress to apply AI techniques in

the Composite Materials area on two levels. At the higher level, we have described the com-

posite materials fabrication life cycle (Figure 1) and discussed a comprehensive problem solv-

ing architecture which will address the entire life cycle (Figure 2, Figure 3, and Figure 6). At

the lower level, we have described results from our initial pilot projects aimed at understanding

problem solving in two well delineated parts of this life cycle. From the first implemented

project, we described research to capture experience-based static design of fabrication plans for

composite materials (Figure 5); from the second implemented project we described research to

capture process control knowledge of cavity tuning for microwave curing (Figure 8, Figure 9,

Figure 10, and Figure 11). Throughout, we have taken a Generic Task viewpoint both in devel-

oping our comprehensive architecture, and in the two specific projects themselves.

Our overall architecture for the composite fabrication life cycle is similar to the (domain in-

dependent) proposal of Elkan [20] who suggests the use of approximate initial planning aug-

mented by incremental detailed planning. Elkan’s idea is that initial planing would thus be

made less computationally expensive, while the overall system would retain its robustness by

making use of the incremental planning when needed. Our division of the composite material

fabrication life cycle into static design of fabrication plans, followed by application of the plans

including replanning has a similar intuition. However, it is more apparent in our domain specif-

ic architecture that the reason the approximate planning will be useful is because it can ignore

many of the details involved with keeping the fabrication process on track. For example, our

static plan designer need not be concerned with issues of material nonuniformity and process-

ing equipment flaws. Deviations from the static fabrication plan caused by such material im-


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perfections will be handled by Local Reactive Planning (Figure 6).

In direct applications of AI to Composite Materials, two recent studies are similar in motiva-

tion to the two parts of our architecture most elaborated to date. Nitsche, Kern, and Janczak [21]

have reported research aimed at the (what we have called static) design of composite materials.

Research at the Wright Patterson Composites Center by Abrams et al [22] has explored the con-

trol of thermal curing of composites by an experience-based approach centered on the measure-

ment of thermal gradients and dielectric constants. Recent results have been impressive in

terms of increased yield of experimental composites [23].

Our agenda for future research is straightforward. On the composite materials side, we are

extending both of our existing projects. For the static fabrication plan project, we are extending

the domain coverage of the current system. In the longer term, we will incorporate a functional

reasoning component into the system. For the microwave mode tuning project, our next step is

to develop the communications capability to actually connect our system with a microwave

curing device. In the longer term, we will expand coverage to include other important problems

of microwave curing, in addition to mode tuning. Ultimately, we intend to implement the entire

composite fabrication life cycle, and to leverage our implementation to design and control

mixed thermal/microwave fabrication of composite materials.

From an AI perspective, we will continue to push against and extend the tools of the Gener-

ic Task Approach. Above, we have noted several possible extensions to the Functional Repre-

sentation Generic Task.. From a larger perspective, we are convinced that the most fruitful in-

vestigations in AI are those that are motivated by real world problems. Our continuing goal is

to apply the broad framework of the Generic Task Approach to realistic problems in the com-

posite materials domain, and to thereby uncover weaknesses in the approach, and in the pro-

cess of correcting these identified weaknesses, to make principled extensions to the GT tools


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and approaches which will enhance problem solving power.

5. ACKNOWLEDGEMENTS

Many of the ideas which are leveraged in research reported have been nurtured by sister

projects which we have in progress to utilize Functional Reasoning in different domains, and

from our collaborators in those domains. We thus are in debt to Dr. G. Philip Robertson,

Kellogg Biological Station, Michigan State University, with whom one of us (Sticklen) is car-

rying on a project aimed at ecological modeling utilizing an functional reasoning approach.

Similarly, we are also in debt to Dr. William Bond, Advanced Computing Group, McDonnell

Douglas Research Laboratory, St. Louis, with whom one of us (Sticken) is carrying on collabo-

rative research aimed at design knowledge capture in the aerospace domain - again from a

functional reasoning perspective. Conversations with John DeLong, Juemin Sun, and Mah-

moud Pegah helped greatly to clarify the ideas presented here.

Research reported here was supported in part by the Research Excellence Fund, State of

Michigan, and by generous equipment grants from Apple Computer. The McDonnell Douglas

Corporation, through its program of supporting academic research, has been a strong supporter

of research within the Michigan State University AI/KBS Laboratory. Initial work on this

project was also supported by the Ameritech Corporation through its program of Ameritech

Fellows.


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6. REFERENCES

[1] Venkatasubramanian, V, Lee, Young, & Gryte, Carl G. (1987) Design of PolymerComposites: A Knowledge-Based Framework. Paper 97e, AIChE Annual Meeting,New York, November, 1987.

[2] Chandrasekaran, B. (1983). Towards a Taxonomy of Problem-Solving Types. AIMagazine. 4 (pp. 9-17).

[3] Chandrasekaran, B. (1985). Generic Tasks in Knowledge-Based Reasoning:Characterizing and Designing Expert Systems at the "Right" Level of Abstraction.Proceedings of The IEEE Second Annual Conference on Artificial IntelligenceApplications.

[4] Chandrasekaran, B. (1986) Generic Tasks in Expert System Design and their Role inExplanation and Problem Solving. Proceedings of The Workshop on the DARPAStrategic Computing Program Experts Systems. McClean, VA.

[5] Chandrasekaran, B. (1986). Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design. IEEE Expert. (pp. 23-30).

[6] Marr, D. (March 1976). Artificial Intelligence -- A Personal View. Technical Report ofthe Massachusetts Institute of Technology Artificial Intelligence Library.

[7] Marr, D. (1976). Early Processing of Visual Information. Phil. Trans. Roy. Soc. 197

[8] Sticklen, Jon, Chandrasekaran, B. & Bond, W. (1989) Distributed Causal Reasoning.International Journal of Man-Machine Studies. 1989.

[9] Sembugamoorthy, V., & Chandrasekaran, B.Functional Representation of Devices andCompilation of Diagnostic Problem-Solving Systems. J. Kolodner, & C. Reisbeck (ed),Experience, Memory, and Learning. Lawrence Erlbaum Associates. 1986.

[10] Sticklen, Jon, & Chandrasekaran, B. Use Of Deep Level Reasoning in MedicalDiagnosis. In Proc. of The Expert Systems in Government Symposium. McLean,Virginia. 1985

[11] Sticklen, Jon & Chandrasekaran, B. Integrating Classification-Based Compiled LevelReasoning with Function-Based Deep Level Reasoning. Applied Artificial Intelligence:3(2). 1989.


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[12] Sticklen, Jon. MDX2: An Integrated Medical Diagnostic System. Ph.D. dissertation.Columbus, OH: Ohio State University. 1987.

[13] Brown, David C. and Chandrasekaran, B. (1986) Knowledge and Control for a Me-chanical Design Expert System. IEEE Expert. July, 1986. Pp 92-100.

[14] Herman, D., Josephson, J. R., & Hartung, R. (1986). Use of DSPL for the Design of aMission Planning Assistant. Proceedings of the IEEE Expert Systems in GovernmentSymposium. (pp. 273-278). McClean, VA.

[15] Chandrasekaran, B., Josephson, J., Keuneke, Anne, & Herman, David. (1989) An Ap-proach to Routine Planning. International Journal of Man-Machine Studies. (pp. 377-398)

[16] Brown, David C. and Chandrasekaran, B. (1986) Knowledge and Control for a Me-chanical Design Expert System. IEEE Expert. July, 1986. Pp 92-100.

[17] Clancey, W. J. (1984). Classification Problem Solving. Proceedings of the AAAI. (pp.49-55).

[18] Asmussen, J. , Lin , H. H., Manring B., Fritz, R., (1987) Single-Mode or ControlledMultimode Microwave cavity Applicators for Precision Material Processing. Rev. SciInstrum. Pp 1477-1486

[19] Montgomery, Carol G. (1947) Techniques of Microwave Measurements. New York: McGraw Hill.

[20] Elkan, Charles. (1990) Incremental, Approximate Planning. Proceedings of AAAI-90.Pp. 145-150.

[21] Nitsche, A.; Kern, H.; Janczak, J. (1990) Composites' Design Based on ExpertKnowledge. Proceedings Of The Fifth Technical Conference Of The American SocietyFor Composites. East Lansing, Michigan. Pp. 382-390.

[22] LeClair, S. R., Abrams, F. L., & Matejka, R. F. (1989). Qualitative ProcessAutomation. AI Edam. 3 (pp. 125-136).

[23] Personal communication from Frances Abrams, WPAFB Materials Laboratory, Dayton,Ohio.


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Fabricating Composite Materials-A Comprehensive Problem-Solving Architecture Based on Generic Tasks

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