Ten CADChallenges

Published by the IEEE Computer Society 0272-1716/05/$20.00 © 2005 IEEE IEEE Computer Graphics and Applications 81

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The world is enamored with the number10. Perhaps it’s because arithmetic is a lot

easier when the number 10 is part of an operation. Ormaybe David Letterman and many others have finallyachieved a long-term impact on society with their topand bottom 10 lists.

Ivan Sutherland used the number 10 to bring signifi-cant consistency to hidden surface algorithms for com-puter graphics in his classic paper.1 It’s in honor ofSutherland—developer of the original Sketchpad appli-cation, the forerunner of today’s computer-aided design(CAD) applications—that we describe our 10 CAD chal-lenges. The concept of CAD itself has expanded intocomputer-aided manufacturing (CAM) and computer-aided engineering (CAE). Basic CAD techniques arereapplied in a number of places, including electrical andmechanical product development, buildings, and enter-tainment. In this article, we focus on mechanical prod-uct development.

CAD, CAM, and CAE generate massive amounts ofdata that must be clearly organized and placed understrict configuration control. The CAD industry also pro-vides support to these functions through product lifecy-cle management (PLM) systems. In assessing thestate-of-the-art CAD, we’re examining a reasonablymature, respected, and accepted form of technology. Theindustry’s multibillion dollar yearly revenues have beenreasonably flat for the past few years. For an introduc-tion to CAD’s history, see the “Brief CAD History” sidebar(next page).

We could place our 10 challenges in a number of dif-ferent categories. We’ve chosen three: computationalgeometry, interactive techniques, and scale. Given ourknowledge of the state of the art, the categories wewould have chosen 10 years ago would likely be differ-ent. Each of the categories presents an opportunity toexpand CAD’s penetration to new user communities andincrease its long-term impact.

Geometry represents the kernel data form that adesigner uses to define a physical product in any CADapplication. Correct assembly of the geometric shape,structure, and system components is the basis for figur-

ing out how to produce the complete product (throughCAM) and how the product will respond once in service(through CAE). Only a few commonly used librariesimplement computational geometry algorithms, anindication of the technology’s relative maturity. Ouranalysis identified three specific geometry challenges:

■ geometry shape control; ■ interoperability across CAD,

CAM, and CAE applications; and■ automatically morphing geome-

try in a meaningful way duringdesign optimization.

The key component of all CADapplications is the end user. Users’success or failure in interacting withCAD products ultimately governs thesuccess of the products themselves.We extend the meaning of interactivetechniques for this article to includemore than low-level input device, display, and human fac-tor issues. We deliberately include the way users workwith applications to accomplish a work task. Using thisextension lets us define three difficult challenges withinteractive techniques:

■ the order and flow of the tasks a user performs toaccomplish something,

■ the relationships among tasks in a complex designenvironment, and

■ the manner in which old designs can effectively seednew ones.

The third category describes the challenge of scale.In many ways, the CAD market has reached a plateaujust because it has not yet discovered ways of goingbeyond its current limits. We introduce scale challengesthat slow the progress of overall CAD penetration.Should these challenges be addressed in a meaningfulway, larger growth in CAD business will likely occur. Thefour scale challenges include

We discuss the significant

technical challenges facing

the CAD industry and

research community and

present an approach to

address these issues.

Editor: Frank Bliss

David J. KasikThe Boeing Company

William BuxtonBuxton Design

David R. FergusonDRF Associates

■ finding ways to cope with vastly larger quantities ofdata,

■ communicating key concepts to user communities tra-ditionally outside the CAD community,

■ reliably migrating data to new versions of softwareand hardware as product life spans increase, and

■ improving productivity for geographically distributedproject teams.

As we built this set of challenges, we realized that thesum of the challenges was greater than we expected.Therefore, we suggest an approach that can help othersunderstand and manage the changes needed. We believethat the technical challenges we’ve identified will leadto fundamental changes in the way people work.

GeometryGeometry lies at the core of all CAD/CAM/CAE sys-

tems and, while not the only item of interest in productdesign and development, it’s the sine qua non of design.Geometry includes both the algorithms and mathe-matical forms used to create, combine, manipulate, andanalyze the geometric properties of objects: points, lines(curves), surfaces, solids, and collections of objects.

Geometry begets three fundamental questions: Whatare the objects to be represented? What mathematicalforms and approximations will be used to representthem? How will information about the representationbe computed and used?

Given the power and generality of current design sys-tems, we can imagine that all issues related to thesequestions had been adequately answered. However, thisis not the case. We discuss three geometry challenges(shape control, interoperability, and geometry in designexploration) that still require extensive research anddevelopment.

HistoryTo illustrate the evolution of geometry methods and

usage we focus on five time periods: pre- and early 19thcentury and early, mid, and late 20th century. In eachof these periods there was a fundamental shift in themethods and usage of geometry driven by new designrequirements.

Prior to the 19th century, the major use of geometrywas to define and maintain line drawings for manufac-turing and records keeping. The tools and methods wererudimentary: Lines were constructed by ruler and com-pass methods and curves were traced from a draftsman’sor loftsman’s spline, a thin wooden or metal strip bentand held to a desired shape using weights (see Figure 1).

In the early 19th century, these methods were aug-mented with descriptive geometry, primarily usingsecond-degree algebraic equations, to provide more pre-cise mathematical descriptions and increase accuracy andprecision in engineering drawings. The objects of interestwere still lines, but descriptive geometry required newtools (for example, slide rules and tables) to aid in calcu-lating various curve properties (such as point location).Descriptive geometry changed from curves drawn onpaper to precise mathematical formulas from which anypoint on a curve could be calculated accurately.

The next major advance took place early in the 20thcentury. Catalogs describing functionality and applica-tion of particular families of curves in engineering beganappearing. The catalogs were based on careful scientific

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Brief CAD HistoryCAD was one of the first computer graphics applications in both

academia and industry. Ivan Sutherland’s Sketchpad at theMassachusetts Institute of Technology and the DAC-1 project atGeneral Motors both started in the early 1960s. Industry developedits own CAD applications, delivered on multiuser mainframes, inthe late 1960s and 1970s. The 1980s turnkey systems bundledhardware and software. Current CAD implementations separatehardware and software components. As a result, CAD softwaremost often executes locally on powerful Unix or Wintelworkstations with specialized 3D accelerator hardware and storesdata on distributed servers.

Even though engineering drawings were the dominant output ofnow-defunct CAD vendors—for example, Lockheed’s CADAM andcommercial companies like AD2000, Applicon, Autotrol,ComputerVision, Gerber IDS, Intergraph, Matra Datavision, andVersaCAD—through the 1980s, early stages of design relied on avariety of unique curve and surface forms. Customized systemswere used in aerospace for surface lofting (such as TX-95 atBoeing) and surface design (such as CADD at McDonnell-Douglas)and in the automotive industry for surface fitting—such as Gordonsurfaces (General Motors), Overhauser surfaces and Coons patches(Ford), and Bezier surfaces (Renault). Current CAD applications relyon more general geometric forms like nonuniform rational b-splines (NURBS).

Solid modeling also started in the late 1960s and early 1970s butfrom different roots. Larry Roberts worked at MIT to automaticallyidentify solids from photographs. The Mathematics ApplicationGroup Inc. used combinatorial solid geometry to define targets fornuclear incident analysis and subsequently developed ray-tracedrendering and solid modeling in Synthavision. Other efforts—forexample, TIPS from Hokkaido University, Build-2 from Cambridge,Part and Assembly Description Language (PADL) from theUniversity of Rochester—had limited industrial impact.

Solid modeling is the 1990s preferred technique for defining 3Dgeometry in small through large companies, and good modelingsoftware is readily available. The use of 3D is common in computeranimation, aerospace, automotive, (and is becoming acceptablefor building architecture), and 2D is also used effectively.

1 Early mean-ing of splineweights.

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analysis and engineering experimentation. For example,the National Advisory Committee for Aeronautics(NACA) generated a catalog of curves that classified air-foil shapes according to flow properties (see Figure 2 andhttp://www.centennialofflight.gov/essay/Evolution_of_Technology/airfoils/Tech5G1.htm and http://www.centennialofflight.gov/essay/Evolution_of_Technology/NACA/Tech1.htm). Objects became more than curves;they also had attached properties that described theirappropriate use in various design activities. Design couldnow begin with curves known a priori to have propertiesneeded for a viable design.

In the 1940s, Roy Liming at North American Aircraftintroduced actual surface representations in his coniclofting system.2 These were not like today’s surface rep-resentations, but his system did provide for completesurface definitions rather than simply a family of lines.It was now possible to think of mathematically com-puting mass, aerodynamic, and hydrodynamic proper-ties. At this point, geometry began changing fromdescribing a physical object to becoming the base forcalculating engineering characteristics. This lessenedthe dependence on physical models and began the pushtoward virtual design. Tools also began changing. Withthe increased emphasis on analysis, calculators andcomputers became required tools.

Liming’s conic lofting methods proved their worth inthe design of the P51 Mustang airplane (see Figure 3).Liming boasted that the Britain-based Mustangs couldfly to Berlin and back because their surface contours didnot deviate from the mathematical ideal.3 However, itwas not possible to visualize the geometry without aphysical prototype or mock-up.

Modern graphics systems made it possible to display3D geometry. Designers could visually inspect designs tofind mismatched or ill-fitting parts and shape defects.The new display technologies required radical changein how geometry was represented: Everything in adesign had to have a precise mathematical representa-tion. Gone were the line drawings used by draftsmen.The old algebraic methods of descriptive geometry weredisplaced by mathematical splines, NURBS, tensor prod-uct splines, and other analytically based forms.

Having a complete mathematical description broughtanother fundamental change. Previously, the preferredmethod of constructing a curve or a surface was highlyintuitive: lay out sequences of points; construct curvesthat pass through the points; and then construct a sur-face from the curves by cross-plotting, conic lofting, orsome other means. Using purely mathematical algo-rithms to interpolate or approximate opened up newpossibilities: Curves could be defined by approximatinga sequence of points, surfaces could be defined by cloudsof points, and geometry could be constrained directlyto satisfy engineering constraints (for example, clear-ances and shape).

Geometric methods and objects used to support prod-uct design and definition have changed significantly. Asdesign objectives and systems continue to respond tothe ever-increasing need to design rapidly, efficiently,and virtually, geometric methods will need to meet thechallenges and change accordingly.

Challenge 1: Shape controlThe success that graphics had in forcing everything

to have a precise mathematical representation actuallyincreased concern over inflections. The hand-drawn andhand-constructed methods, including Liming, hadimplicit control of shape whereas the new, polynomialand piecewise polynomial methods (splines, B-splines,and so on) did not. Inflections in a curve passing througha sequence of data points are possible using polynomi-als or piecewise polynomials even though the datapoints do not suggest inflections. Therefore, it becameimportant to devise algorithms that not only fit pointsbut also allowed shape control. Shape control defines

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2 NACA Cata-log of AirfoilShapes.

3 P51 Mustangs.

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the occurrence and position of inflection points (pointsat which the signed curvature of a planar curvechanges). The control is needed for both engineeringand manufacturing optimization.

Researchers have proposed various schemes for shapecontrol. Most failed because there were always cases forwhich the methods failed to properly preserve shape.Many methods exist for detecting shape anomalies afterthe fact, and a person must fix the anomalies by hand.Removing the person in the loop lets geometry passdirectly to other applications for optimization. The chal-lenge becomes finding algorithms that avoid anomaliesin the first place.

Attempts to modify the methods used previously toaccount for shape control did not work in general, andit wasn’t until the 1980s that we realized the basic prin-ciples of those methods were wrong.4 Although nonlin-ear methods4 have proven themselves in a variety ofshape control situations, most of these methods havenot made their way into commercial CAD systems.

Challenge 2: InteroperabilityCurrent CAD systems do not integrate well with CAE

analysis (such as structural mechanics, fluid dynamics,and electromagnetics).5 For example, computationalfluid dynamics (CFD) must interrogate geometry quick-ly and reliably. Most CFD codes construct a computa-tional grid from the geometry. Building the grid reliablymeans that there should be no unintended holes in thegeometry—that is, the geometry should be what CFDpractitioners refer to as watertight. Real geometry fromCAD systems is rarely watertight.

Geometry from one CAD system is difficult to trans-late reliably into another. Estimates peg the cost of inter-operability in the US auto industry at $1 billion per year.6

Holes, translation errors, and other problems arisefrom two major sources: floating-point arithmetic andtolerances. Floating-point arithmetic, which forcesapproximations in numerical calculation, is addressabletheoretically but not practically. We could impose high-er precision (double, triple, and so on) to drive downthe resulting errors. Or we could change to a rationalarithmetic system and eliminate the need for floatingpoint. Digital floating-point arithmetic is a research areaby itself.7

Tolerances control the accuracy of computed solu-tions and are a fact of life in today’s CAD systems. A sim-ple example involves calculating the curve ofintersection between two surfaces. When the algebraicequations representing the geometry are simple (forexample, a plane or a circle), a closed-form solution forthe intersection exists. However, closed-form solutionsgenerally do not exist for operations on equations of asufficiently high degree (for example, intersecting twobicubic surfaces). Computing the intersection curve usesapproximation, a problem independent of precision.Some CAD systems will recompute intersection curvesif more accuracy is needed. This doesn’t solve the prob-lem, especially if the intersection curve is used to gen-erate other geometry.

Tolerances are needed to control the approximation.8

Too loose a tolerance can give results that are fast but

incorrect. Too tight a tolerance can result in poor per-formance or failure to converge. Even seemingly simplesurface-to-surface intersections become difficultbecause of choosing tolerances.

Tolerances determine the success of downstreamengineering (CFD, finite element) and manufacturing(numerical control programming, quality assurance)analyses. Selecting a tolerance that guarantees a highprobability of success requires that the geometry gen-erator understand the kinds of analyses to be employed,the environment of the analyses, and even the specificsoftware to be used a priori.

In summary, digital arithmetic and current math the-ory are insufficient to perform reliably for complexgeometry operations and to interoperate well withdownstream analysis software. The geometry must be aswatertight as possible for downstream use, and algo-rithms cannot result in topological inconsistencies (forexample, self-intersections and overlaps). The challengeis to find ways to deal with poor results. Perhaps a newmath theory that has closed-form solutions for complexsurface operations and supports watertight represen-tations for downstream analysis is the way to addressthis challenge.

Challenge 3: Design explorationAutomated design exploration through multidisci-

plinary optimization presents the third challenge.Design optimization requires that geometry remainstopologically valid as parameters are perturbed whilepreserving the designer’s intent. There are two aspectsto consider: how to parameterize the geometry fordownstream analysis and how to structure geometryalgorithms to support continuous morphing, a key toany optimization process. The former is primarily anengineering function, which we do not discuss here.

Morphing is a requirement that CAD systems do notcurrently support. Morphing algorithms today allow thehole in the upper block to flip into the lower block whenthe edges of the two blocks align. This is fine geometri-cally. However, this is a disaster for optimization,because the geometry does not morph continuouslywith the parameters.9

The challenge is to design and build geometry sys-tems that ensure the continuity of morphing operations.Morphing continuity differs from geometric continuity.Geometry often has discontinuities (for example, tan-gents) that must be preserved during morphing. Mor-phing continuity means that the geometry doesn’tchange suddenly as parameters change.

Parameter values must be simultaneously set to rea-sonable values to ensure valid geometry for analysis andoptimization. Automating morphing is a challengebecause CAD systems have evolved as interactive sys-tems that let users fix poor results. Design optimizationneeds a geometry system that automatically varies para-meters without user guidance and yet maintains designintegrity and intent.

Multidisciplinary design causes us to rethink the geo-metric design process as well as the algorithms. For exam-ple, many CAD systems use a piecewise quadratic or cubicalgorithm for defining a curve through a sequence of

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points. These algorithms will not reproduce an embed-ded straight line exactly. Preserving embedded line seg-ments forces the curve fit algorithm to be modifiedwhenever three successive points lie on or are near (deter-mined by some system tolerance) a straight line.

Figure 4 contains a second example of geometry thatseems reasonable but causes problems during morph-ing. Consider the surface H(x, y, z) = y − ax2 = 0 so itsintersection with the z = 0 plane is described by the func-tion f(x) = ax2. Parameter a is a shape parameter thatvaries according to an optimization process. Supposethe algorithm for approximating intersection curvesuses piecewise straight lines and the fewest join pointspossible to achieve a certain tolerance. Now suppose thetolerance is 0.5. For values of a approximately equal to1, the approximation will alternate between a functionwith constant value 0.5—that is, a spline with noknots—and a piecewise linear function—that is, a splinewith one interior knot. In other words, varying the para-meter a from slightly less than 1 to slightly greater than1 causes the model to change discontinuously. As a pass-es through the value 1, the intersection curve not onlychanges shape (the blue line morphs to the red line inFigure 4), but its properties (for example, arc length)change discontinuously. This algorithm design producesgood static approximations but fails during morphing.

SummaryAddressing the geometry challenges outlined here

will not be just a simple task of going through andimproving or deleting offending algorithms. It alsorequires a fundamental rethinking of how geometrydesign systems should work.

Interactive techniquesSignificant improvements in interaction are not going

to be achieved by making more efficient menus or a bet-ter mouse. Rather, they are going to depend on rethink-ing the nature of the process. Three specific challengesresult:

■ changing the order of workflow,■ understanding the concept of place in the workplace,

and■ intentional design.

History Interaction technology has evolved slowly over the past

40 years. Input devices have not changed significantly.Physical buttons, such as keyboards, function keys, voice,fingers, and so on, let people talk to the machine. Graph-ical pointers come in numerous shapes and sizes and letus move in two or three dimensions, with similar resolu-tion to early devices. The graphical user interface hasremained essentially unchanged since 1984.

Displays have gotten a lot smaller and a lot larger.Smaller displays are ubiquitous because of the phe-nomenal growth in the cell phone industry; larger dis-play penetration is steadily growing. The basicresolution of display devices (number of units per squareinch) is about the same as Sutherland’s Sketchpad wasin the early 1960s.

Improvements have been achieved largely by addingnew functionality, enhanced graphic design, and betterflow of control. However, systems are not easier to use,and the demands on the user today might be even high-er than 20 years ago. The complexity of designs, data,and geometry are growing as fast, or faster, than thepower of the tools to handle it.

Nevertheless, some things are changing, and thesechanges will afford the potential to break out of this sit-uation. For example, more than 10 years ago, NicholasNegroponte challenged people to imagine what wouldbe possible if bandwidth was essentially free. The onlything that matched how amazing and unlikely that con-cept was at the time was its prescience.

The parallel challenge today would be, “Imagine whatwould be possible if screen real-estate were essentiallyfree.” Already, paper movie posters are being replacedby $10,000 plasma panels. Imagine the potential impactwhen the cost of a comparable display drops two ordersof magnitude and it is cheaper to mount a 100-dpi dis-play on your wall than it is to mount a conventionalwhiteboard today.

Large displays will be embedded in the architecture ofour workspace. We will stroll through and interact withsuch spaces with agile small, portable, wireless devices.And many of the changes that are going to affect thefuture of CAD will emerge from the evolving behaviorof both people and devices as they function within them.

Within this context, a divide-and-conquer approachwill be used to address the complexity posed by today’sCAD systems. While power will come in numbers, mostwill be relatively inexpensive and target a particularfunction. The isolated gadgets that first emerge as add-ons to existing systems will morph into the keystones ofa new mosaic of integrated technologies that will trans-form the process.

To realize the potential of this, the following three chal-lenges involve thinking about evolution in a human-, nottechnology-, centric way.

Challenge 4: Reversing engineering Important changes do not result from doing the same

things faster or for less money. They come when you flipapproaches and methods on their head, that is, we mustconstantly ask ourselves, Do we do things the way thatwe do today because it is the right way, or because it’sthe only way that we knew how when we started?

The inertia of the status quo blinds us to the recogni-tion that major changes, such as those due to Moore’s

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4 Blue curvemorphs discon-tinuously intored curve.

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law, open up different and desirable approaches. If weovercome that inertia, we can explore other approaches.

In the field of aerodynamics, for example, it’s trulyimportant to get designs that are efficient and safe. Gov-ernment agencies such as NASA and companies such asBoeing spend millions of dollars on tools, such as windtunnels and CFD analysis, that help them test such sys-tems. However, these tests typically come late in thedesign process. Now combine this fact with one of themost basic rules of design: The later in the process thata mistake is detected, the more expensive it is to fix.10

Replacing this rigorous testing at the back end is notthe answer, but it would be more efficient if technologyexisted that allowed preliminary tests on the desktop oreven on PDAs. Designers could then catch bad designsmuch earlier in the process. This would also help themdiscover, explore, refine, and understand the mostpromising designs as early in the process as possible.

This approach to CFD could be applied to a number ofother parts of the CAD workflow. Consider the ability tointroduce elements such as stress testing, volume orstrength calculations, and so on, earlier in the workflow.This would allow designers to consider the intercon-nection and interoperability of parts much earlier in thedesign cycle than is currently the case.

Adding simulation earlier into the design process willenable consideration of behavior, rather than just form,to play into the process. The fundamental change inworkflow is the essential interactive technique that willmake the geometry design exploration challenge of theprevious section a useful and usable capability.

Challenge 5: Everything in its placeThere was a time when someone’s location in a CAD

environment indicated their current job. For example, inan automotive design studio, there is often one locationwhere people deal with interiors, another contains thefull-sized clay models, and some other location is set upfor exploring colors.

However, the typical modern CAD environment con-tains a uniform sea of anonymous cubicles or desks. Ingeneral, anyone walking through such a space will notlikely be able to tell if they are in the accounting or theengineering department. Because people don’t movearound anyhow and are essentially anchored to theirdesk, this organization isn’t important in some ways.Yet, in the midst of all of this, we hear an ever-louder callfor collaboration. We also hear of the emergence of ubiq-uitous computing. How do these two points relate, if atall, in terms of transforming the CAD workplace?

To begin with, ubiquitous computing will break thechain that anchors the engineer to a general-purposeworkstation. This change will not just enable but neces-sitate designer’s moving from one specialized area toanother in a manner harkening back to the best of pastpractice.

Not only will the workspace be broken up into spe-cialized areas with specialized tools, but these tools willalso consist of a combination of private and public dis-plays and technologies. Some will be mobile and othersembedded in the environment. Examples of the latterwould include large format displays that function as dig-

ital corkboards, surfaces where you can view large partson a 1:1 scale, and areas where you can generate phys-ical parts using a 3D printer.

The physical mobility of a person and data can great-ly impact agility of thought. Mobility brings increasedopportunity for collaboration and increased visibility ofa particular activity. Rather than design a system thatlets us send more email or documentation to the personat the other side of the studio, the studio should bedesigned so that we have a greater probability and oppor-tunity to bump into and work with that person face toface. If we are going to have work-across sites, then link-ing designers’ efforts must be linked automatically as aconsequence of undertaking a particular activity.

Our notion of space is not about making things moreabstract or virtual. Rather, it concerns the recognitionand exploitation of the attributes and affordances ofmovement and location in a technology-augmentedconventional architectural space. We discuss the impactof collaboration among the geographically distributedin challenge 10.

The challenge to future systems is as much abouthuman–human interaction as it is about new forms ofinteraction between human and machine.

Challenge 6: What we doCAD companies might view themselves as primarily

purveyors of tools for the creation of high-quality 3Dmodels. Consequently, if they want to grow their busi-ness, they might conclude that they should make a bet-ter, more usable modeler.

However, this stream of thought might be as wrongas it is reasonable. Consider the following questions:

■ Is there a shortage of trained people to fill the existingdemand for creating 3D models?

■ Are there things that need to be modeled that cannotbe built by existing users with their current skills andtools?

■ Is there a huge untapped market for 3D models wait-ing for an easy-to-use modeling package that takeslittle training to use?

In general, the basic answer to all of these questionsis “No.” It’s not modeling—at least in the sense that itexists in today’s CAD packages—that lies behind any ofthe fundamental challenges outlined in this article.

In fact, it might well be that all of these questions arepoorly posed, since they all assume that the intent of theuser is modeling. It is not. Rather, it’s getting a productor a part made. Modeling is just one way of doing so,and in many cases, not always the best way.

We are then challenged to answer this better ques-tion: What besides modeling from scratch might enableus to achieve our product design? A good answer to thatquestion should lead to a more innovative and effectivesolution than today’s design-from-scratch modelingapproach.

Our favorite recourse in such cases is to look to thepast. In this case, the clue lies in Figure 2, the NACA cat-alog of airfoil sections. As we discussed previously, suchcatalogs let designers build an aircraft by selecting com-

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ponents with known properties rather than workingfrom scratch.

We suggest moving back to this approach—but withappropriate modifications. As product complexityincreases, the CAD process will subdivide into those spe-cialists who design and build components (often out ofsubcomponents) and those who make larger assembliesout of them. However, the components in future CADsystems will not be fixed objects made up of what com-puter scientists might call declarative data. Rather, theywill include a strong procedural component.

The basic problem is that objects, even those as sim-ple as an airfoil, don’t easily scale. If you make exactlythe same form, but larger, the plane might not fly. A sim-ple example is the hummingbird. It can definitely fly.However, if you scaled it up in size by a factor of 10, itcould not.

In this object-oriented process, designers generallydo more than just select components and plug them intoa larger assembly. Rather, they take components andtransform them into what is needed. The componentsact as a starting point, something designers work from,rather than start from scratch.

Given that physical products don’t scale, one keyaspect of component design is the component’s embed-ded capacity to be transformed along meaningful anddesired dimensions, while maintaining specific toler-ance, performance characteristics, manufacturability,maintainability, and so on.

Embedded in the component catalog, therefore, arejust not the parts, but the knowledge of how parts can betransformed while maintaining essential properties.Consequently, the tools of the CAD engineer not onlyhelp the designer find the appropriate components butalso support transforming them in such a way as tomaintain the desired properties.

Finding, cloning, modifying, morphing, and adapt-ing will largely replace constructing from scratch. In sodoing, the assumption is that methods such as aerody-namic testing, stress analysis, and others, can occur inadvance for each component and carry over into anassembly. This approach transforms the current prac-tices of who does what, where, when, why, and how.

SummaryEmerging user-interface technology will allow us to

transform how we work. Furthermore, the new tech-nology can help us implement the transformation with-out any major discontinuity with respect to currentpractice.

Our sense and analysis of interaction needs to switchfrom how we interact with a specific computer or pack-age to how we interact as people, how we interact withchange, and how we interact with our materials—atwhat level, in what way, and to what objective.

ScaleThe challenge of scale is one that scientists and engi-

neers continue to encounter as they push the limits ofmacro- and nanotechnology. Moore’s law governs theexpansion of computing hardware’s limits. Advancedhardware lets us produce larger quantities of data. Soft-

ware advances tend to lag behind more powerful hard-ware, yet we seem to be able to consume computinghardware resources at a rate that always leavesCAD/CAM users begging for larger storage capacity,greater network bandwidth, and better performance.

Managing scale results in a delicate balancing act ofknowing when “good enough” occurs. While there arenumerous dimensions of scale in CAD/CAM/CAE, theprimary areas of challenge today include

■ Sheer data quantity that can exceed a project team’ssoftware, hardware, and cognitive capacity.

■ Making critical data comprehensible to other users.■ Keeping data meaningful as product life spans

become longer.■ Collaboration with a geographically distributed work-

force.

The challenges of scale in this section derive fromexperience at Boeing, a large and varied aerospace man-ufacturer. While Boeing does not represent the norm,similar problems exist in automobile production, ship-building, and other manufacturing companies. Moreimportantly, solving a Boeing-sized problem has oftenresulted in breakthroughs that have profoundly affect-ed smaller efforts.

HistoryScale has grown as the overall process of designing,

building, and maintaining complex products haschanged.

Humans have designed and built extraordinarily com-plex artifacts throughout history. The seven wonders ofthe ancient world are clear examples: Roman aqueductsstill deliver water service to Italian cities, and the GreatWall of China still stands, acting as a tourist destinationand is clearly visible from the ground and Earth orbit.

Documentation on the ancient design process issketchy, but became more formal during the Renais-sance. Leonardo da Vinci’s sketches of various mechan-ical possibilities captured a more formal depiction andallowed designers to analyze possible configurationstrengths and weaknesses before construction started.These practices continued to evolve into highly accuraterenderings documented as engineering drawings in thefirst half of the 20th century. The evolution of compu-tational geometry described earlier has taken us to thepoint we are now in the 21st century.

The challenges we discuss in the rest of this sectionare the direct result of change in the fundamentaldesign-analyze-build-maintain process. Until the latterpart of the 20th century, highly complex projects fea-tured integrated design-build teams. Slave labor wasthe dominant force in the earliest build teams, and on-site designers oversaw every detail of construction.Large efforts took a long time and involved thousands ofworkers. There has been a desire to decrease construc-tion time and the number of people ever since.

As increased specialization in individual aspectsbecame more prevalent, principal design and assem-bly continued to exist within close geographic prox-imity. Consider the evolution of design-build at

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Boeing. Boeing’s first airplanes were designed andbuilt in the Red Barn (see Figure 5). As build problemsoccurred, engineers could walk to the factory to makeon-site corrections.

As automation and the complexity of the productincreased, later airplane generations continued to bedesigned and built in the relatively close proximity ofthe Puget Sound region in Washington. Boeing locatedthe engineers designing the 737 and 757 within min-utes of the Renton assembly plant; Everett housed the747, 767, and 777. These engineers were responsible forthe design of all of the major sections of all airplanes.

With the 787, its next-generation airplane, Boeing isdistributing the design, manufacture, and assembly ofmajor subsections across the world. Each major suppli-er will deliver preassembled sections of the airplane tofinal assembly, rather than delivering smaller compo-nents as occurred with previous models. This approachwill dramatically reduce final assembly times. Comput-ing and communications systems are mandatory for thisto occur, and huge amounts of data must be integratedand managed. Because the design is completely digitaland 3D, a larger variety of people will look at images ofthe data during the 787’s life cycle. Time and distancecomplete the notion of scale: Mechanical products builttoday have longer and longer lifetimes, and geograph-ic distance separates people.

Challenge 7: Understanding vast quantities ofdata

Design, engineering, manufacturing, and mainte-nance processes have routinely existed in virtual isola-tion from one another. Part of the scope challengerelates to data, especially as products become complex.Another part relates to people because the graphicsvocabulary used in the design process is dramaticallydifferent from the one used when the product is beingmanufactured and assembled.

Early efforts in product design used the waterfallmodel. In this approach, designers handed drawings to

engineers for analysis leading to design improvements.The drawings then made their way to manufacturingplanners, people making and assembling parts, and oth-ers responsible for production maintenance. The late1980s saw a change in the designer-engineering analy-sis interface because of the advent of 3D modeling sys-tems with enough capacity to generate and manage anentire commercial airplane. Downstream users contin-ued with master definitions represented as 2D drawings.

Initial efforts to deal with issues like manufactura-bility and maintainability focused on integrated design-build teams. The team members used differentapplications and kept data in segregated areas with dif-ferent configuration management schemes.

CAD/CAM software vendors are starting to addressthis problem with product lifecycle management (PLM)systems that extend previous product data management(PDM) systems. PLM systems let companies store cus-tomer requirements, design geometry, engineeringanalysis results, manufacturing plans, factory processdesigns, maintenance designs, and so on, in a singlerepository that infuses configuration managementthroughout.

PLM systems are difficult to sell. The essential bene-fit (managing data across key aspects of a manufactur-ing company) generates more cost in each individualarea but decreases overall product costs through forcedpre-facto integration and better configuration manage-ment and visibility. PLM systems compete with otherlarge corporate systems for managing personnel, enter-prise resource planning (ERP), supply chain manage-ment (SCM), and so on. Personnel management, PLM,ERP, and SCM systems all replicate significant portionsof the same data. Finally, getting new technology sold,especially when the technology works behind thescenes, is always a difficult problem.

The real problems occur after product integrationbecomes institutionalized. Integrated products must beexamined from a number of different views. Each grouphas a different purpose and wants to believe that theirview is the master. For example, a commercial airplanehas the following views:

■ As-designed view (engineering bill of materials). Therelationships among individual components reflect alogical organization of the data (primarily geometry)as modified to satisfy engineering improvements foraerodynamics, structural strength, weight, and so on.

■ As-ordered view (manufacturing bill-of-materials).The relationships among individual componentsreflect the parts that must be ordered and the specif-ic attributes (including geometric definition) neededto successfully acquire the parts. Concepts like alter-nate suppliers are important in this view.

■ As-delivered view. This view contains informationabout the physical configuration turned over to a cus-tomer after the assembly process is complete. Somecomponents might have been replaced (for example,a supplier changed) or modified during assembly.

■ As-owned view. The actual components in a final prod-uct change over time. Components get routinelyreplaced or repaired as maintenance occurs.

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Each of these views is essentialduring a product’s life cycle and eachwants to be master. Providing eachview without negatively impactingthe integrity of the underlying dataposes a difficult challenge.

In terms of sheer storage space,the amount of data needed to faith-fully represent a product and toshow how decisions were reached inits design is expanding by decimalorders of magnitude. The dataranges from marketing intelligenceabout customers to versions of com-plex geometry to multiple data setsfrom engineering analysis runs tomanufacturing planning scripts to sensors monitoringmanufacturing processes, physical testing, and real-timeproduct health.

In addition to managing multiple configurations andviews, people face a challenge when they have to inter-pret this vast quantity of information. For example, tech-nology is on the horizon that will allow real-timedisplay11,12 of an entire commercial airplane model.Humans have a finite ability to comprehend complexi-ty, and understanding how to best display the data in ameaningful way requires extensive work.

Challenge 8: Appropriate designs for other usesand users

This challenge focuses on the large number of peoplewho are removed from the data creation and analysisprocess and rely on the digital product definition for theirown job tasks. These people participate in sales, fabri-cation, assembly, certification, maintenance, and prod-uct operation. In the case of a commercial airplane, theuser community extends far beyond company bound-aries. For example, airline personnel buy Boeing prod-ucts, perform maintenance, order spares, train pilots andattendants, and so on. Partners design and analyze sig-nificant airplane components. Suppliers formulate pro-posals to fabricate parts and assemble components.Government agencies certify that the final product per-forms according to published regulations and guidelines.

Many of these groups have their own graphic vocab-ulary because different information must be communi-cated to different audiences. Consider the followingexamples.

Engineers and designers commonly use images likeFigure 6a. The colors are meaningful in terms of theengineering and design functions of individual compo-nents, but not for manufacturing or maintenance. Thisis a significant change from practice prior to 1990, wherethe master communication mechanism was the engi-neering drawing (see Figure 6b). The authority imagefor certification, manufacturing, and maintenance is stillthe engineering drawing. Communicating via the engi-neering drawing to a maintenance engineer causes aradical transformation, as Figure 6c shows.

Figures 6b and 6c are clear illustrations of this chal-lenge. The two represent exactly the same 3D geome-try. However, the graphic styles are dramatically

different. Tools are commonly available to work withthe base 3D model—for example, make everything in ascene translucent but objects of interest; show and hide;rotate, scale, and translate; and measure. We can gen-erate the basic 3D hidden line image for Figure 6c, dosome early guesses to explode parts, and so on.

These tools fundamentally change the basic style inwhich the graphic image is rendered, and some mighteven produce engineering drawings. None contains anability to adapt an image for specific uses or users. There-fore, the challenge lies in knowing how to draw theimage to communicate to a specific user communitybecause the communication techniques and standardsvary significantly from community to community andfrom company to company.

As a result, companies spend significant amounts oftime and labor retouching or redrawing images.Automation also offers the possibility of providing newtypes of images that might be more task appropriate.

Generating the initial image itself presents a signifi-cant challenge. The taxonomy of effective graphic images(Figure 6 represents a small subset) is much larger. Mas-sironi13 has developed an excellent taxonomy that delin-eates the types of images and the fundamental graphictechniques people have used to communicate visually.

Challenge 9: Retrieving data years laterToday’s product specifications for tolerance, fit, reli-

ability, and so on, are greatly different than they were40 years ago. For example, the Boeing 707 successful-ly introduced commercial aviation to the jet age. Yet the707’s part fit was loose enough that it received the nick-name “the flying shim.” On the other hand, the first Boe-ing 777 fit together so precisely (largely due to the useof CAD/CAM techniques from 10 years ago) that thenumber of discrepancies needing redesign was sub-stantially less than what had appeared to be anextremely optimistic early prediction. Rather than themultiple mock-ups needed for previous models, the 777manufacturing mock-up flew as part of the flight certi-fication process. Similar stories exist in the automotiveand other industries.

One constant remains: The engineering drawingserves as the design, manufacturing, and certificationauthority. While we realize that the engineering draw-ing has its limits, it has another important attribute: It

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6 Communicating data: (a) solid shaded image, (b) engineering drawing, and (c) stylizedmaintenance image.

(a) (b) (c)

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can be archived for long periods of time and still beunderstood. The Mylar film that Boeing uses for its per-manent records lasts longer than the 50-year life spanof commercial airplanes.

In contrast, consider the challenge if the archives werestored digitally. The media itself is not the problembecause it can be routinely copied to new media. Thereal challenge is changing software versions. Users rou-tinely expect that some percentage of their data will notmigrate successfully from one version to the next. Somealgorithms will be tweaked, and what worked in the lastversion doesn’t in the next (or vice versa). A substantialversion change or choosing a different software suppli-er means massive amounts of data conversion andrework. The net result is that companies cannot affordto change to a new system version from the same vendoruntil years after initial release, let alone change to a newvendor entirely. As the amount of configuration-man-aged data increases, the data migration challengebecomes even more pronounced.

Challenge 10: Limited by the speed of lightChallenge 5 discussed the impact of users being

released from the constraints of single-user desktop andlaptop computers into an environment where work-spaces can be shared. This challenge addresses the usersworking in a geographically distributed environment,where walking across the hall becomes impossible.

A continued debate rages about the internationaliza-tion of large and small businesses throughout the world.In spite of specific national interests and boundaries,companies (many of which are small and mid-sized) areacquiring design, manufacturing, assembly, mainte-nance, and customer support service help throughoutthe world. The incentives range from specific technicalskills to trade offsets to cheaper labor.

Doing effective distance collaboration for 3D design,which lacks the immediacy and extensive cues of aface-to-face session, is a long-term research and cul-tural challenge. The key productivity aspect forimproved design cycle time is collaboration across anumber of different stakeholders. There are a widevariety of collaboration models and a reasonableamount of research done in computer-supported col-laborative work. Researchers are starting to pay atten-tion to collaborative task analysis.14 However, researchon how to make such efforts more effective on a glob-al scale is in its infancy, and the cognitive effects of dis-tance on a collaborative 3D work environment havenot been addressed.

SummaryThis final set of challenges indicates that the way

groups of people work on design problems and thelongevity of the electronic versions of their products isundergoing a dramatic change. The magnitude of thechallenges these changes are causing is at least as greatas getting people to adopt CAD in the first placebecause the challenges impact the fundamental waypeople work.

How to proceed: Spurn the incrementalWe conclude by suggesting an approach that can

address our 10 challenges. The basic CAD businessmodel is incremental refinement, and it can only takeus so far.

Incremental refinement is based on bringing out then + 1st version, where the new version release hasimprovements. For this model to work, the incrementalimprovement for releasen, ∆In, must be greater thansome threshold value, VT, which represents the mini-mum improvement that will still motivate a company topurchase the new version.

The cost of achieving that degree of improvementincreases with each release and can be approximated by

In other words, to keep the incremental value ofreleasen high enough to motivate purchases, the cost ofthose improvements is on the order of the cost of thoseof the second release (that is, the first upgrade), raisedto the order’s nth power.

Two factors contribute to this cost picture. First, as sys-tems go through successive releases, they grow in com-plexity. This negatively affects their malleability andincreases the cost of adding value or refactoring the soft-ware or basic functionality. It’s not just the number of linesof code or additional features that cause this. As a systemapproaches maturity, the legacy of the initial underlyingarchitecture, technologies, and paradigms creates astraightjacket that severely affects the cost of change.

Next, as products mature, the software more or lessworks as intended and markets approach saturation. Formost users, the current version of the software is goodenough. Changes increasingly tend toward tweaks andtuning rather than major improvements. Or they aredirected at more specialized functions needed by asmaller segment of the market. Consequently, there isless to motivate most customers to upgrade.

As the product reaches late maturity, the accumulat-ed impact is that development costs increase while thesize of the addressable market decreases. Software salesare no longer sufficient to cover the growing develop-ment costs. At this point, companies increasingly relyon annual support contracts, a model difficult to sus-tain. At best, the switch to support revenue delays thecollision of technology costs and economic viability.Even when a company totally reimplements its productfor a new version, the tasks the user performs are gen-erally the same as in the previous version and often giveless functionality than the last version of the old system.

The CAD industry is reaching this state. How do weget to the next level, the one that should address thesechallenges?

Order-of-magnitude approachIf incremental refinement won’t work, perhaps it will

come from some new breakthrough. Perhaps some newinvention will magically appear and save the day. TheNational Academy of Science recently released a reportthat studied the genesis of a large number of technolo-gies, including graphical user interfaces, portable com-

∆ ≥ ⇒ ∆ = ∆I VT I In n

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munication, relational databases, and so on. For each,it looked at how long that technology took to get fromdiscovery to a $1 billion industry. The average wasabout 20 years.15 This suggests that any technologiesthat are going to have impact over the next 10 years arealready known and have probably been known for atleast 10 years.

We believe that the answer is not new technologiesbut new insights into technologies that are alreadyknown, whose potential is perhaps not appreciated, orwhich have so far not been technically or economicallyfeasible. The engineering of significantly better prod-ucts should come from fresh insights on what is alreadyknown or knowable.

By analogy, think about the marks on the door frameof the kitchen closet where parents record the growthof their children. Without that documentation, espe-cially because you live with them day-to-day, you gen-erally don’t notice the changes. Long after it has actuallyhappened, by measuring them again, you notice thatthey have passed on to the next stage of growth. Thisalso happens in living with technology.

Our observation leads to the order-of-magnitude(OOM) rule, which says: If anything changes by an orderof magnitude along any dimension, it is no longer thesame thing.

OOM is a way to measure significance of the types ofchanges needed to meet the technical challenges we’vedescribed. Exploiting this rule forces us to notice OOMchanges and understand their implications. However,it also lies in teasing out less obvious, but meaningfuldimensions, along which to test for such OOM changes.

Because we have all been so intimately involved withthese challenges and CAD technology, we have nottaken the time to create the measures that allow us tosee where profound changes have already occurred andwhere they need to occur. We believe that the approach-es needed to address the 10 challenges are most likely toresult from rethinking things from a human-centricrather than a technology-centric perspective.

ConclusionThe next wave in CAD will come about largely

through the cumulative effect of the introduction of anumber of small, lightweight technologies that collec-tively form a synergistic mosaic, rather than due to theintroduction of some monolithic new technology. Thevalue of this approach is that it can be introduced incre-mentally, without requiring some disruptive disconti-nuity in skills and production. Thus, change will occurthrough radical evolution. That is, incremental evolu-tionary change will happen in a way that leads us to aradically new approach to working.

There will be changes in how we do things, and thesewill significantly affect the nature of CAD systems andhow they function. We suggest that using OOM offers astrategy to cause revolution in an evolutionary manner.Ultimate success will happen if and only if all of the tech-nical areas in which OOM changes occur are kept in bal-ance. We conclude that institutionalizing radicalevolution is the top CAD challenge we face in 2005,2015, and beyond. ■

References1. I. Sutherland, R. Sproull, and R. Schumacker, “A Charac-

terization of Ten Hidden-surface Algorithms,” ACM Com-puting Surveys, vol. 6, no. 1, Mar. 1974, pp. 1-55.

2. R.A. Liming, Practical Analytic Geometry with Applicationsto Aircraft, Macmillan, 1944.

3. P. Garrison, “How the P-51 Mustang Was Born,” Air & SpaceMagazine, Aug./Sept. 1996.

4. D.R. Ferguson, P.D. Frank, and A.K. Jones, “Surface ShapeControl Using Constrained Optimization on the B-splineRepresentation,” Computer Aided Geometric Design, vol. 5,no. 2, 1988, pp. 87-103.

5. R.T. Farouki,“Closing the Gap between CAD Model andDownstream Application,” SIAM News, vol. 32, no. 5, 1999,pp. 303-319.

6. Research Triangle Inst., Interoperability Cost Analysis ofthe US Automotive Supply Chain, Nat’l Inst. of Science &Technology, March 1999; http://www.mel.nist.gov/msid/sima/interop_costs.pdf.

7. C.M. Hoffmann, Geometric and Solid Modeling: An Intro-duction, Morgan Kaufmann, 1989.

8. D.R. Ferguson et al., “Algorithmic Tolerances and Seman-tics in Data Exchange,” Proc. 13th ACM Symp. Computa-tional Geometry, ACM Press, 1997, pp. 403-405.

9. V. Shapiro, Limitations in Current CAD Systems; http://sal-cnc.me.wisc.edu/Research/parametric/limits.html.

10. F. Brooks, The Mythical Man-Month: Essays on SoftwareEngineering, Addison-Wesley, 1975.

11. W. Baxter et al., “GigaWalk: Interactive Walkthrough ofComplex Environments,” Proc. Eurographics Workshop onRendering, ACM Int’l Conf. Proceeding Series, 2002, pp.203-214.

12. I. Wald, C. Benthin, and P. Slusallek, “Distributed Interac-tive Ray Tracing of Dynamic Scenes,” Proc. IEEE Symp. Par-allel and Large-Data Visualization and Graphics (PVG), IEEEPress, 2003.

13. M. Massironi, The Psychology of Graphic Images: Seeing,Drawing, Communicating, Lawrence Erlbaum Assoc., 2002.

14. D. Pinelle, C. Gutwin, and S. Greenberg, “Task Analysisfor Groupware Usability Evaluation: Modeling Shared-Workspace Tasks with Mechanics of Collaboration,” ACMTrans. Computer–Human Interaction, vol. 10, no. 4, 2003,pp. 281-311.

15. Computer Science and Telecommunications Board, “Com-puter Science and Telecommunications Board of theNational Research Council,” Innovation in InformationTechnology, National Academies Press, 2003.

David J. Kasik is an architect forsoftware engineering, geometry andvisualization, and user-interfacetechnology at the Boeing Commer-cial Airplanes Group InformationSystems. His research interestsinclude an innovative combination

of basic technologies and increasing awareness of theimpact of computing innovation outside the computingcommunity. Kasik has a BA in quantitative studies fromthe Johns Hopkins University and an MS in computer sci-ence from the University of Colorado. He is a member of

IEEE Computer Graphics and Applications 91

the ACM, ACM Siggraph, and ACMSIGCHI and received a Siggraph Spe-cial Recognition Award in 1992 foracting as Liaison to the ExhibitorAdvisory Committee. Contact him atDavid.j.kasik@boeing.com.

William Bux-

ton is principalof his own bou-tique design andconsulting firm,Buxton Design,where his time is

split between working for clients, writ-ing, and lecturing. He is also a chiefscientist for Bruce Mau Design ofToronto, and he is an associate pro-fessor in the Department of Comput-er Science at the University of Toronto.Buxton has a B.Mus. from QueensUniversity and an MSc in computerscience from the University of Toronto.He received the 1995 CanadianHuman–Computer CommunicationsSociety Award, the 2000 New MediaVisionary of the Year Award, and is amember of the CHI Academy. Contacthim at bill@billbuxton.com.

David R. Fer-

guson wasBoeing’s leaderin geometryresearch anddevelopmentuntil his retire-

ment. His research interests includethe mathematics of curves and sur-faces with special emphasis on non-linear methods for geometricconstruction. Ferguson has a BS inmathematics from Seattle University,and an MS and PhD in mathematicsfrom the University of Wisconsin. Heis an editor of Computer Aided Geo-metric Design. Contact him atDRF.ASSOC@comcast.net.

For further information on this or anyother computing topic, please visitour Digital Library at http://www.computer.org/publications/dlib.

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January/February: Emerging Technologies*This issue covers the Siggraph 2004 Emerging Technolo-

gies exhibit, where the graphics community demonstratesinnovative approaches to interactivity in robotics, graphics,music, audio, displays, haptics, sensors, gaming, the Web,AI, visualization, collaborative environments, and enter-tainment.

*Bonus CD-ROM of demos included with this issue.

March/April: State-of-the-Art Graphics This issue covers an array state-of-the-art computer graph-

ics, including new developments in VR, visualization, andnovel applications. The broad range of topics highlights theusefulness of computer graphics.

May/June: Smart Depiction for VisualCommunication

Smart depiction systems are computer algorithms andinterfaces that embody principles and techniques fromgraphic design, visual art, perceptual psychology, and cog-nitive science. This special issue presents such systems thathold the potential for significantly reducing the time andeffort required to generate rich and effective visual content.

July/August: Applications of Large DisplaysThe emergence of large displays holds the promise of

basking us in rich and dynamic visual landscapes of infor-mation, art, and entertainment. How will our viewing andinteraction experiences change when large displays are intro-duced in our workplace, home, and commercial settings?This special issue will serve to collect and focus the effortsof researchers and practitioners on the frontier of designinglarge displays.

September/October: Computer Graphics inEducation

Graphics educators are cultivating the next generation ofdevelopers. However, hardware and software barriers to entryhave shrunk, and people from nongraphics areas have begunadopting the technology. This special issue will highlightapproaches from inside computer graphics education and usesfrom outside the field in domain-specific education.

November/December: Moving MixedReality into the Real World

As computing and sensing technologies become faster,smaller, and less expensive, researchers and designers areapplying mixed reality technology to real problems in realenvironments, This special issue will present a broad rangeof issues faced by designers as they move state-of-the-arttechnology beyond the laboratory.

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