Knowledge-Based Engineering (KBE) Design Methodology at the Undergraduate and Graduate Levels* D. E. CALKINS University of Washington, Mechanical Engineering Department, Box 3526000, Seattle, WA 98195, USA NATHANIEL EGGING and CHRISTIAN SCHOLZ Sandia National Laboratories, MS 9105 P.O. Box 969, Livermore, CA 94550, USA. E-mail: [email protected]An emerging design technology known as knowledge-based engineering (KBE) is the next step beyond CAD for product representation. KBE allows a true generative virtual prototype to be developed that represents both the geometric and the non-geometric characteristics of a product. Both an undergraduate and graduate level design course based on this technology is described. A new version of the design process is presented for the development of a virtual prototype. Examples of products (systems) that were modeled include a hand held vacuum and a parametric human which are presented and described. BACKGROUND THE DEVELOPMENT of complex systems requires a sequence of engineering and manage- ment decisions which must satisfy many competing requirements. Design is recognized as the primary contributor to the final product form, cost, relia- bility and market acceptance. The high-level en- gineering design and analysis process (conceptual design phase) is particularly important since the majority of the life-cycle costs and overall quality of the system are determined during this phase. The major opportunities for cost savings occur in the earliest phases of a product design. Approxi- mately seventy per cent of the life-cycle costs are frozen by the end of the conceptual design phase, Fig. 1. The key to shortening the design cycle is to shorten the conceptual design phase, which will also reduce the amount of engineering in the redesign stage. The engineering trade-off process during conceptual design is undertaken using good esti- mations and informal heuristics. Current tradi- tional CAD tool support is extremely limited for the conceptual design phase. There is need to rapidly conduct design analyses involving multiple disciplines communicating together (trading off such things as performance, cost, reliability, etc.). Finally, it is necessary to be able to manage a large amount of domain-specific knowledge. The solu- tion is to commit more resources at the conceptual design stage to reduce the cycle time by eliminating redesign. All of these factors argue for an integrated design tool and environment that can help make decisions early in the design synthesis (conceptual design) process. This integrated design tool will enable a diverse and multi-disciplinary team of engineers, designers and stylists to achieve consen- sus of design intent under complex design require- ments and increased design constraints. The design tool should allow the design team to examine more configurations at greater levels of detail. The problem then is to develop an archi- tecture for a design tool that meets all of these requirements. SYSTEMS DESIGN Design process A process is defined as an ordered set of steps that are performed to accomplish a task, i.e. the design of a product. The steps in a process that are intended to define how each step is to be accom- plished. Design methodologies accomplish that task. While many models have been proposed for the design process, the one used attempts to incorporate current technology and tools available for design, Fig. 2. The process shown consists of six steps starting with ‘Problem Definition’ and ending with ‘Prototyping.’ Design process stages The design of a product traditionally proceeds through a series of well defined stages or phases including: . conceptual design (concept exploration and development); * Accepted 10 October 1999. 21 Int. J. Engng Ed. Vol. 16, No. 1, pp. 21–38, 2000 0949-149X/91 $3.00+0.00 Printed in Great Britain. # 2000 TEMPUS Publications.
Transcript
Knowledge-Based Engineering (KBE)Design Methodology at the Undergraduateand Graduate Levels*
D. E. CALKINSUniversity of Washington, Mechanical Engineering Department, Box 3526000, Seattle, WA 98195, USANATHANIEL EGGING and CHRISTIAN SCHOLZSandia National Laboratories, MS 9105 P.O. Box 969, Livermore, CA 94550, USA. E-mail:[email protected]
An emerging design technology known as knowledge-based engineering (KBE) is the next stepbeyond CAD for product representation. KBE allows a true generative virtual prototype to bedeveloped that represents both the geometric and the non-geometric characteristics of a product.Both an undergraduate and graduate level design course based on this technology is described. Anew version of the design process is presented for the development of a virtual prototype. Examplesof products (systems) that were modeled include a hand held vacuum and a parametric humanwhich are presented and described.
BACKGROUND
THE DEVELOPMENT of complex systemsrequires a sequence of engineering and manage-ment decisions which must satisfy many competingrequirements. Design is recognized as the primarycontributor to the final product form, cost, relia-bility and market acceptance. The high-level en-gineering design and analysis process (conceptualdesign phase) is particularly important since themajority of the life-cycle costs and overall qualityof the system are determined during this phase.The major opportunities for cost savings occur inthe earliest phases of a product design. Approxi-mately seventy per cent of the life-cycle costs arefrozen by the end of the conceptual design phase,Fig. 1. The key to shortening the design cycle isto shorten the conceptual design phase, whichwill also reduce the amount of engineering in theredesign stage.
The engineering trade-off process duringconceptual design is undertaken using good esti-mations and informal heuristics. Current tradi-tional CAD tool support is extremely limited forthe conceptual design phase. There is need torapidly conduct design analyses involving multipledisciplines communicating together (trading offsuch things as performance, cost, reliability, etc.).Finally, it is necessary to be able to manage a largeamount of domain-specific knowledge. The solu-tion is to commit more resources at the conceptualdesign stage to reduce the cycle time by eliminatingredesign.
All of these factors argue for an integrated
design tool and environment that can help makedecisions early in the design synthesis (conceptualdesign) process. This integrated design tool willenable a diverse and multi-disciplinary team ofengineers, designers and stylists to achieve consen-sus of design intent under complex design require-ments and increased design constraints. Thedesign tool should allow the design team toexamine more configurations at greater levels ofdetail. The problem then is to develop an archi-tecture for a design tool that meets all of theserequirements.
SYSTEMS DESIGN
Design processA process is defined as an ordered set of steps
that are performed to accomplish a task, i.e. thedesign of a product. The steps in a process that areintended to define how each step is to be accom-plished. Design methodologies accomplish thattask. While many models have been proposed forthe design process, the one used attempts toincorporate current technology and tools availablefor design, Fig. 2. The process shown consists ofsix steps starting with `Problem Definition' andending with `Prototyping.'
Design process stagesThe design of a product traditionally proceeds
through a series of well defined stages or phasesincluding:
. conceptual design (concept exploration anddevelopment);* Accepted 10 October 1999.
21
Int. J. Engng Ed. Vol. 16, No. 1, pp. 21±38, 2000 0949-149X/91 $3.00+0.00Printed in Great Britain. # 2000 TEMPUS Publications.
. preliminary design;
. detail design (production design).
The distinction between these stages is related tothe level of design detail that is examined withregard to the system and its components.
Concept, or conceptual design, deals with devel-opment of a system at its very highest level, usuallywith a very coarse representation with only themajor subsystems represented.
Preliminary design proceeds to the next level ofrepresentation and is also known as embodimentdesign.
Detail design includes analysis and results in adesign description at a level suitable for manufac-ture. The arrangement, form, dimensions, toler-ance and surface properties of individual parts arespecified. Materials and manufacturing processes,
and part assembly procedures, are also specified.Key factors in detail design are:
. standards
. standard components
. tolerances
. materials
. manufacturing processes.
Design typesTypes of design include:
. parametric design
. routine design
. selection or component design
. prototype-oriented design.
Routine design, which comprises 80% of theengineering activity, is based on minor variations
Fig. 2. Design process.
Fig. 1. Conceptual design effect on life cycle cost.
D. Calkins, et al.22
of pre-existing practice or procedure. Knowledge-based engineering (KBE) is the tool that is used forroutine design where the product (system) is com-plex and has many assemblies, subassemblies, etc.
Current design toolsMany design tools have been developed to aid
the design engineering in the process of design.These include 2-D CAD, 3-D CAD includingwireframe, surface and solid modeling CAD, 2-DNURBS-based systems and parametric modeling,all of which are geometry based. CAD systemshave been key in the development of draftingautomation, but are not able to deal with know-ledge such as rules, engineering practices or manu-facturing processes. Parametric modeling CADsystems are based on the geometrical relationshipsbetween parts. Parts can be transformed by vary-ing dimensions, thereby making changes easier andfaster in the traditional design process. However,they do not easily manage the non-geometricalknowledge about a product design.
Product design knowledge must also includenon-geometrical knowledge about the systemperformance to truly reflect the design intent.This aspect of the design process analysis hasbeen traditionally addressed by applying proce-dural languages such as FORTRAN, Pascal orC. The procedural languages are powerful toolsfor engineering analysis applications, but have noinherent geometric capability. Another limitationof current CAD/CAE techniques is that they arenot appropriate at the concept or early stagedesign level of complex systems.
VIRTUAL (DIGITAL) PROTOTYPE MODEL
What is needed is a way to represent the productdesign process to obtain a true virtual prototypewhich would allow the early development andevaluation of a product. The virtual prototypewould replace traditional physical prototypes andallow the design engineer to examine `what-if'scenarios while iteratively updating their designs.A true virtual prototype would not only representthe shape and form, i.e. the geometry, it would alsorepresent non-geometric attributes such as weight,material, performance and manufacturing pro-cesses. Designers want a design representationthat will be an exact representation of a physicalprototype with both geometrical and non-geome-trical attributes.
Product representation has moved from the 2-Dorthographic drawing representation of the shapeand form of the geometry, to full 3-D modelrepresentation of the geometry. The design toolthat is needed for the design engineering domain,clearly must have attributes of all of the tools justdiscussed. It must combine the geometrical repre-sentation of the CAD systems, be able to do theengineering analysis of the procedural languagesand represent the design knowledge as in an expert
system. A true virtual prototype contains this fullrange of design knowledge.
ENABLING TECHNOLOGIES FOR DESIGN
Types of knowledge`Knowledge', as applied to KBE, can be divided
into four types [1]:
. facts,
. procedures,
. judgments,
. control.
Formalized knowledge found in handbookssuch as material specifications, engineering data,ASTM standards, and equipment specifications isconsidered factual knowledge. Algorithmic andoperative knowledge are the two forms of pro-cedural knowledge. Numeric and non-numericprocedures for solving a problem or accomplishingsome end are all elements of algorithmic proce-dural knowledge (APK). Facts are transformed byAPK through engineering and analysis algorithms.Operative procedural knowledge (OPK) is used tocreate, delete, and transport facts. Examples ofOPK programs are finite-element analysis, optimi-zation, and database management systems [1].Rules of thumb and common best-practices areexamples of judgment knowledge. Heuristics,observations, experience, and plausible reasoningare also included in judgment knowledge. Logicand the formal principles of reasoning are funda-mental to judgment knowledge application.Control knowledge is metaknowledge or know-ledge about knowledge. The other types of know-ledge are managed by control knowledge. Patterndirected actions, anticipation of unexpected devel-opments, and dealing with uncertainties are allfeatures of control knowledge [1].
Knowledge-based engineering (KBE)The technology that allows the development of a
true virtual prototype of a product is known asknowledge-based engineering, or KBE. KBE is themethodology for capturing and structuring know-ledge about a design and its design process. KBEmay be used to define engineering methods andprocedures [2]. In KBE, the product structure tree(topology) is dynamic, so that KBE offers trueengineering automation including applicationdevelopment, geometric modeling, applicationdeployment and tools integration. Knowledge-based engineering is a programming tool used todevelop a virtual prototype or a design advisor forthe design of an established product in a givendesign domain. Dym, et al. [3] and Gonzalez, et al.[4] provide valuable overviews of KBE.
Existing knowledge about a class of designs isutilized in knowledge-based engineering or design(KBE or KBD) and organized into a databaseformat usable by computers. Detailed designs orvirtual prototypes are then rapidly developed
through the use of digital computing power, devel-oped databases, and systems of rules. The productmodel which is developed in the KBE environmentis a virtual prototype. A virtual prototype has all ofthe geometric characteristics or attributes of theproduct as well as the non-geometric attributessuch as materials, mass properties, stress anddeflection characteristics, etc. Once the virtualprototype is created, it can be used by the designersto evaluate the success or merit of the designconfiguration, and then modify it if desired. Theproduct model represents the engineering intentbehind a geometric design. The informationcontained in a product model includes physicalattributes like geometry, material type and func-tional constrains.
Generative technologyThere are three types of KBE tools that are
currently being explored and developed. Theseinclude:
The expert system was the first type of tooldeveloped for use in the engineering domain.This tool is used for diagnostic purposes such asanalyzing a malfunctioning automobile engine.
The second type, design advisor, is the one to morecurrent developments. It is used to follow the designprocess of a system, and advise the designer ofconstraint and rules violations based on rules con-tained with the design advisor. The designer thenacts on this advice and makes appropriate changes.
The third type involves developing a model ofthe system based on rules contained with themodel. This model, a virtual prototype, thenreacts to changes in attributes (either geometricor non-geometric), and regenerating a newinstance of the prototype. This is the type ofKBE that is used in the classes developed.
KBE uses generative technology to capturegeneric product design information, includinggeometry and topology, product structure devel-opment and manufacturing processes as designrules. Generative modeling maps functional speci-fications to a detailed representation of theproduct. The advantage of a generative model isthat as the product requirements change, thedesign representation is immediately updateddirectly affecting all outputs. Thus, KBE is adynamic object model wherein the representationof the design is continually updated. KBE metho-dology facilities the capture of engineering andmanufacturing knowledge into a generativemodel by rapidly generating new designs fromfunctional specifications. In contrast to theconventional design tools, KBE offers true designautomation vs. design assistance. KBE is a robustdesign technology for continuous redesign of asystem during the design process.
KBE product representationCurrent KBE software is based on an object-
oriented non-procedural design language such asLISP. As a result, the design information need notbe ordered correctly within the model, as it willwork out the order itself. Object-oriented pro-gramming works on the concept of objects thatare used to represent the characteristics, bothgeometric and non-geometric, of actual physicalobjects. Objects are not passive, but can react withother objects. An object can create and storeinformation and act in response to external stimuli.An object can demand information from anotherobject, or send information to another object.
KBE enables true concurrent engineering bycapturing the domain expertise of a range of con-tributors in an organization. This can includerepresentatives from design, engineering, toolingand other areas of manufacturing. KBE vendorshave a well-established methodology for capturingand codifying this range of product information.Often, KBE developers will collaborate with meth-odology consultants to learn the `knowledgecapture' process on a first development projectand then will transfer and apply those skills tofollow-on projects.
KBE toolsThere are a variety of software tools available
for KBE tool development. Included are ICADTM,TKSolverTM, Design LinkTM, ProEngineerTM,STONEruleTM and Smart ElementsTM. All ofthese are integrated with at least one of thecontemporary CAD systems to provide a contem-porary integrated design system. Unigraphics,CATIATM, Pro-EngineerTM, IDEASTM and Auto-CADTM are some of the options.
These software tools are used to develop domain-specific design tools of the two KBE approaches,design advisor and the virtual prototype.
Generative virtual prototype (GVP)The virtual prototype approach forms the basis
of the KBE classes described, and is based on theuse of the KBE software ICAD [5]. ICAD is usedfor meta-design, which is the design of design toolsin the form of a product model. The product modelis the framework for the product structure, engin-eering analysis, product cost, design standards,regulatory codes, material characteristics, manu-facturing constrains and process plans. It is able tooutput a design report that represents the designstate of the product. This report can include forexample: data for analysis, 3-D geometric models,bills of material, cost reports and manufacturinginstructions. The GVP captures and automates thefunctional design rules and understood methodol-ogies of the engineering process. The GVPprovides functionally valid alternatives for engi-neers to select and manipulate. The engineers addtheir judgement to optimize final systems designs.
A generative virtual prototype (GVP) is a systemmodel that represents both the geometric and non-
D. Calkins, et al.24
geometric attributes of a product (an object) whichare embedded in the KBE model. It stores know-ledge about a system in a product model composedof design and manufacturing engineering rules,which address both geometric and non-geometricissues. A generative virtual prototype is a com-bination of these design rules and includes a set ofengineering instructions used to create the design,that is, the vehicle geometry. The generative virtualprototype represents the engineering intent behindthe geometric design. It can store product informa-tion such as geometry and material specificationsas well as process and performance information.
The generative virtual prototype paradigm isdefined as follows:
Generative: generate or automatically produce aninstance of the virtual prototype in response to aninput state vector. Take input specifications, applyrelevant procedures and generate a design auto-matically. When the requirements change, thedesign is updated immediately along with all ofperformance outputs.Virtual: in effect although not in actual fact: acomputer-based modelPrototype: original model or example of a parti-cular type.
Design rulesKBE is based on the use of design knowledge in
the form of `design rules'. The design rules formthe kernel of an object. Design rules comprise fourbasic categories:
1. Heuristics: comprised of experimental rules ofthumb and `best practices.' Usually based oncorporate culture design heuristics. These are ofthe type, If (condition is true), then (actionrecommended).
2. Empirical design rules: these rules are based oncurve-fitted expressions that are developedfrom experimental data. Meta-model technol-ogy used to develop models of complex systems.
3. Legislated constraints: these are comprised ofrules established by law or by engineeringstandards.
4. Laws of physics: based on first principles in theform of analytical or numerical models. Alsoknown as parametric rules. These rules areusually simple algorithms that would besolved using spreadsheet models.
Design rules are used to synthesize the knowledgein the knowledge base and to establish how theknowledge is used in a given model. The designrules are used to both define and relate theattributes in a KBE model. The methods andprocesses of an engineer are mimicked by theserules. Design rule types include:
. calculations
. conditionals
. look-up databases
. fixed
. variable
. references
. execute external programs
. selections
. optimizations.
KNOWLEDGE-BASED ENGINEERINGUNDERGRADUATE AND GRADUATE
DESIGN COURSES
KBE undergraduate and graduate coursesThe Department of Mechanical Engineering at
the University of Washington has recognized theimportance of KBE in engineering, and both anundergraduate and a graduate level design classhave been developed and offered. These include aspecial version of ME 495 senior level under-graduate capstone design class and ME 570, agraduate level design class. Both classes coverboth KBE technology and its application byhaving the students go through the process ofdeveloping a product model, or virtual prototype.
Two different software tools were used in eachclass. The graduate course, which was developedand offered first, used the ICAD software with thesupport of Knowledge Technologies Inc. throughthe donation of the software ICAD. While ICADis the industry standard tool for generative tech-nology development, it has problems when used inthe academic environment. It is expensive, andbased on the UNIX environment, thus requiringan expensive workstation. We have only onesystem available for the class. A PC/NT-basedsolution was developed and tried in the under-graduate offering. This included the use of threesoftware programs, TKSolverTM/Design LinkTM
for the rule base and ProEngineerTM as the geome-try engine. This proved to be a satisfactory low-cost solution for the single offering of the under-graduate class version.
Course description, format and grading processThe basis of the course is for a team of students,
at each level, to develop a KBE generative virtualprototype. The difference between the undergrad-uate and graduate levels is the complexity of thesystem to be modeled, and the number of designrules that are embedded in the model. The coursefollows a seminar format with assigned readingsand discussions. The students work together indesign teams on a quarter-long design project.The student's grade depends on participation inthe assigned group project. There are varioustechnical article readings that are assigned duringthe quarter, as well as technical informationpackages. The student is expected to do researchboth at the engineering library aswell, ason the Web.The students are formed into design teams of twoand are expected to work together. At the end of thequarter, each team submits a formal design technicalreport and makes a formal technical presentation.Each team makes progress report presentationsduring the quarter and is graded by the other class
members on their oral presentation skills. The gradeis based on technical content and communicationskills (oral, written and graphical). The grade forthis class depends on the following items:
. quality of work (technical content)
. writing communication skills
. oral presentation skills
. team participation
The students are informed that they can spend agreat deal of time working long hours on theproject. However their grade depends entirely onhow they communicate that information andknowledge through their oral presentations andwritten reports. `This is how it is in industry, andthis is how it is in the class.' The students must alsodocument their work thoroughly and keep aDesign Notebook and Journal.
Course syllabus
1. Introduction to KBE2. Design Process Models3. Product Structure (Hierarchical) Decomposition4. Design taxonomy: Environment, Problem &
Process5. Design Knowledge6. Artificial Intelligence (AI)7. Design Tools8. Design Support System (DSS) Technology9. Synthesis: Feature Based Parametric Geometry
Modeling10. Analysis: Simulation11. KBE Tools
KBE COURSE TECHNOLOGIES
The course embodies several technologies inaddition to its main focus of KBE. These technol-ogies include product structure decomposition,metamodel technology and feature-based para-metric geometry modeling.
Product structure decompositionThe concept of the virtual prototype may be
related to the stages of the design process in itsdepth of representation. For example, if a product
undergoes a process of decomposition down to itsindividual parts, Fig. 3, we see that each level ofdecomposition represents the product system atthe various stages of design. For example, at itshighest level, only the main sub-systems are repre-sented and therefore correspond to the high-levelor conceptual design stage. Thus the levels of detailof the design representation correspond to thedevelopment of the virtual prototype at theconceptual, preliminary and detail design stages.
This decomposition representation of theproduct is known as the product structure treeand represents the topology of the product.
Meta-model technologyMeta-model technology is used to develop the
engineering analysis modules for the non-geometric attributes of the virtual prototype.Metamodels are approximating (empirical)models of engineering product or subsystemperformance. They are used to approximate theresponse surface through the generation of designrules which are developed into meta-models toevaluate the performance of the main sub-systems.
Meta-models may be developed from heuristicknowledge using empirical data, or from morecomplex simulations such as FEA or CFD.
Feature-based parametric geometry modelerComplex geometry may be modeled by decom-
posing the surface geometry into control curveswhich are defined by design `features'. These`features' include information on position, slopeand curvature of each control curve. The value ofthese `features' establishes a design state for thesurface geometry. The control curve uses basisfunctions, usually polynomials, to define theirshape. The feature value set is then used toquantify the control curves to generate the geome-try. The designer must simply specify numericalvalues of the features to generate an instance of asystem geometry.
KBE DESIGN PROCESS
A knowledge-based engineering design processwas developed and used that reflects the architecture
Fig. 3. Product structure diagram.
D. Calkins, et al.26
of the development of a KBE virtual productdesign process, Fig. 4. The process outlines theprocedures that are used by the students over thequarter to guide them through the development ofthe virtual prototype.
Product decompositionThe product selected for the class virtual proto-
type exercise has to meet several criteria. First, awell established product was chosen so that multi-ple examples could be used for the product decom-position exercise. Second, the product must not beoverly complex due to the time constraints of a ten-week quarter. Consumer products fulfilled theserequirements. Thus far, two products have beenused, a bicycle and a Black and Decker hand-vacuum.
Product decomposition is the process of dis-assembling a product and constructing a hierarch-ical representation of the product assemblies, sub-assemblies and parts. Product decomposition is auseful benchmarking tool as the assemblies andparts are analyzed and evaluated during theprocess. Therefore, decomposition is often usedduring the development of new or improvedproducts to help engineers determine the state-of-the-art in a given design or application.
The purpose of decomposition is two-fold. First,the engineer is able to determine the form of theproduct and its component parts. The shapes ofthe physical entities that make up the product aredescribed by the form. Second, insight is given bydecomposition into the function of each entity.The tasks the entities perform is described by afunction. The form and function of the Black andDecker VP300 hand-vac was determined byproduct decomposition. During decomposition,the assemblies, sub-assemblies, and parts weredetermined and the form and function of eachwere noted. The parts were then mapped out in aproduct decomposition chart, Fig. 5. The vacuumwas made up of two assemblies, six subassembliesand thirty-four parts. A simple and efficient pack-aging design was exhibited by the vacuum. Thematerials used in the hand-vac, with the exception
of the batteries and motor, were plastic, copper,bronze, and ceramic. Copper leads were used toconduct battery current. A 7.2 V Johnson motorwas powered by two 3.6 V Nickel-Cadmium(NiCAD) rechargeable batteries. Suction wasprovided by a centrifugal fan.
Virtual prototype model scopingModel scoping is the first step in the develop-
ment of a virtual prototype KBE model. This is theprocess in which the range and scope of the KBEmodel are established by the engineer. As much oras little of the hierarchical tree as desired can beincluded in the KBE model. The entities within thechosen portion of the tree are incorporated into thevirtual prototype. The level of the product struc-ture diagram chosen to develop a VP correspondsclosely to the stages in the design process. A top-level abstraction represents the product at theconceptual design stage, while a subassembly
might represent the product at the detail designstage. In the case of the example used for the class,the top level was chosen, Fig. 6. The motorassembly, fan, batteries, case/handle, filter, andnozzle were included in the KBE model of thehand-vac, Fig. 7.
This was deemed a realistic model for the scopeof the course which enabled the students to apply
the principles of KBE within the confines of theacademic quarter.
Geometrical and non-geometrical attributesGeometrical and non-geometrical attributes are
closely related to product form and function. Thegeometries of the product entities are described bythe geometric attributes. Height, length, width,
Fig. 6. Hand vac product decomposition (tree chart representation).
volume, and area are some of these attributes.Attributes unrelated to the geometry aredescribed by non-geometric attributes. Weight,material, function, cost, and manufacturabilityare included in the non-geometric attributes.The attributes are identified during productdecomposition when the component parts andassemblies are most easily observed. Once thegeometric and non-geometric attributes are iden-tified, they are then examined to determine whichattributes are interrelated. Readily changeableattributes are also identified.
An understanding of part attributes and theirrelationships is key to the development of a virtualprototype.
GENERATIVE VIRTUAL PROTOTYPEDEVELOPMENT PROCESS
Knowledge base developmentKnowledge base development is the process of
gathering knowledge and information pertinent tothe design. The information necessary for thedesign and development of a given product virtualprototype is contained within the product know-ledge base. Traditional and non-traditional meansare techniques used to develop a knowledge base.Research, consulting, and benchmarking existingdesigns are included in traditional means. Solu-tions and company-specific solution techniquesto past design problems are also considered atraditional knowledge-base development tech-nique. Internet exploration and on-line data collec-tion are considered non-traditional means.
Hand vacuum knowledge baseDue to limited time, a knowledge base was
developed for only three of the six componentschosen for the KBE model. These componentsinvolved the batteries, motor and fan. Batteryresearch was conducted using the Internet, libraryand vendor data. The knowledge base was furtheradded to by benchmarking the battery types usedin a wide range of hand vacuums. A knowledgebase for an extensive array of battery sizes, chemi-cal compositions, and manufacturers was thusdeveloped. Information regarding battery perfor-mance, capacity, size, cost and durability arecontained within the knowledge base.
The knowledge bases for the motor and fanwere developed in a similar manner. Informationregarding motor sizes, specifications, and perfor-mance characteristics was obtained from the Inter-net. Information regarding fan performance andflow characteristics was obtained from the engin-eering library. In addition, tests were run on thefan/motor assembly to ascertain knowledge aboutthe performance of the two components in con-junction. Torque, voltage, current, and velocitydata were obtained in the tests. The test datawere disseminated to the students via an informa-tion packet.
Knowledge base filteringKnowledge base filtering is the process of filter-
ing and condensing the knowledge base to extractthe knowledge to be used directly in the KBEmodel. The knowledge base is filtered in a varietyof ways. Trend analysis is used to filter largequantities of data by examining trends establishedamong common data types. These trends can beobserved by plotting the data; equations can beextracted from the plot and observed. Anotherfiltering method is through selection. Selection isthe process of selecting the knowledge necessaryfor a given KBE model. An example of selection isthe narrowing of battery types to include onlyNickel Metal Hydrides (NiMH) and NiCAD. Inorder for selection to take place, there must besome understanding of how the final model willwork. As a greater understanding of the problem isgained, natural filtering occurs. As a result, know-ledge that is unimportant or irrelevant to themodel is abandoned.
The knowledge base for the hand vacuum wasfiltered using all of the above techniques. Data forthe batteries was filtered using selection and trendanalysis. NiMH and NiCAD batteries were chosento be used in the model. Thus information regard-ing these batteries was selected from the knowledgebase. NiMH and NiCAD battery sizes capable ofpowering a hand vacuum were also selected fromthe knowledge base. Trend analysis for thebatteries was a two-phase process. Curve-fittingvolume versus capacity for the selected batterytypes was accomplished in the first phase. Adirect correlation between battery diameter andsize (A, AA, AAA, etc.) was revealed in thesecond phase. Batteries of similar diameter werefound to have similar overall characteristics. Thesebatteries were grouped by size and averages weredetermined for capacity, mass, length, and massdensity. The battery data were greatly reduced bythis analysis.
The knowledge base for performance character-istics of the fan and motor were determined usinganother technique. In this case experimental testswere conducted to develop a database which inturn was used to formulate empirical relationships,or design rules. These empirical relationships formthe basis of the fan/motor metamodel. Theserelationships included the motor/fan RPM as afunction of input battery voltage and the currentdrawn as a function of the motor/fan rpm. It wasshown that the performance characteristics of thefan and motor found in the vendor data wereaccurately represented by the fan/motor tests.Therefore, knowledge for the fan and motor wasfiltered to only include the relations developedduring the fan/motor testing.
Design rule abstractionDesign rule abstraction is the process of
abstracting or generalizing the rules and represen-tation of parts within a particular model. All levelsof modeling from individual parts to the entire
assembly can be abstracted. A vacuum modelcontaining only six components, motor, fan,handle/casing, filter, nozzle, and batteries, wasdeveloped through abstraction. These six compo-nents were all directly influenced by changes to theinputs of the KBE model. Those componentsassociated with the chosen six that were notaffected by the inputs were eliminated throughabstraction. Wiring, switches, and screws were alleliminated in the abstraction process. The geome-tries of the six components were also abstracted.The batteries, motor, and fan were represented ascylinders. The casing, nozzle, and filter were repre-sented as a box, truncated wedge, and truncatedcone. In this manner, the model was simplified andmade to focus on the important relationshipswithin. There are many types of design ruleswithin each rule category.
GRADUATE LEVEL HAND-VAC VIRTUALPROTOTYPE DEVELOPMENT
Hand-vac parametric geometry modelFeature-based parametric modeling is a special
class of design rules relating solely to geometry.The parameters are usually linear or radial dimen-sions, geometric relationships (tangent, concentric,parallel, etc.), or equations. In order to develop atrue KBE model, a program must be used thatallows both geometric and non-geometric featuresto be modeled; thus, a parametric CAD program isnot suitable for KBE. As part of the developmentof the hand-vac model, geometric relationshipswere established linking the various parts andfeatures of the hand-vac. First, the hand-vac wassimplified by approximating it with variousgeometric primitives. These primitives were thenmapped out parametrically and the relationshipsnoted for use in the virtual prototype. The firstletter of a part name is incorporated in its para-metric labels. The casing, for example, has ahole in one side with a diameter labeled `C6'.In the parameter list C6 is listed as equaling I3,which is the exterior diameter of the impeller part.These relationships were incorporated into thevirtual prototype to establish the geometric rulesneeded.
Hand-vac inputs and outputsThe determination of the inputs and outputs of a
particular model is the first step in design ruledevelopment. The particular attributes or func-tional values are entered into the model as inputsto generate a certain output. All steps in designrule development are facilitated by the input/output determination. The goal is to correctlydevelop the design rules so that proper output isattained given the input to the model. The inputsfor the hand-vac were divided into customer andmanufacturer-driven categories. As the productbeing modeled is a home appliance, it was feltthat the model input should reflect the concerns
of customer focus groups that assist in the productdesign. Both manufacturer and customer concernsare addressed by establishing inputs for each. Thefollowing inputs were established for the handvacuum:
Customer-driven inputs are:
. Run time: desired run time of the hand vacuumunder full load, (min.)
. Nozzle capacity: dirt capacity the nozzle canhold, (in3)
. Suction pressure: pressure at the nozzle tip, (psi)
Manufacturer-driven inputs are:
. Battery type: NiMH or NiCAD
. Battery size: AAA, AA, A, etc
. Unified/scattered battery grouping: batteriesplaced in single or multiple locations
. Casing thickness: material thickness of the plas-tic case, (in.)
Issues concerning the average customer arereflected in the customer-driven inputs. Issuesthat impact production cost, material cost, anddesign concerns are reflected in the manufacturer-driven inputs. The outputs listed below are gener-ated from the above inputs:
. Number of batteries
. Position of batteries (in.)
. Nozzle volume, (in.3)
. Nozzle intake area (in.2)
. Overall vacuum dimensions, (in.)
. Center of gravity location, (in.)
. Weight, (lb.)
. Cost, ($)
. Mass moments of inertia, (in.-lb.-sec.2)
Hand-vac design rule abstractionTwo factors are critical to the development of
the design rules. The first is an understanding ofthe attributes and attribute relations of each part.The second is complete knowledge of the inputsand outputs of the model. Design rules are devel-oped once these factors are accounted for. Designrules are developed in two phases. The first phaseis rule generation for the attributes of the assem-blies, subassemblies and parts. The proper designrule type is dependent on the attribute beingmodeled. The attributes in the model are manipu-lated by rules that are generated in the secondphase. Changes are imparted on the attributes of amodel given changes to the inputs. The proper typeof manipulation rule is dependent on the outputdesired from the model. The hand vacuum ruleswere developed using the above criteria. Designrules for the attributes were established and themanipulation rules were developed to achieve thedesired output. The rules developed for each of thesix parts of the hand-vac are discussed below.Many of the rules are taken from the parametricgeometry representation of the hand-vac. Thedesign rule types and examples for the hand-vacare given in Table 1.
D. Calkins, et al.30
Hand-Vac ICAD constructsA superset of LISP, ICAD Design Language
(IDL) is the programming language used in ICADto allow users to construct KBE models of parts orsystems using geometric or non-geometric rules.ICAD modeling includes the following constructs:
. Defpart: a defpart (`definition of a part') is adescription of a component of a product model,where the product model is a collection ofdefparts. a defpart includes the following:
. Input-attributes: describes the inputs that mustbe specified to create an instance of the defpart.
. Attributes: describes the engineering rules in thedefpart.
. Parts: describes the components of the defpart,the product structure tree that is created when adesign instance of the defpart is generated.
. Defun: a defun (`define function') is used todefine a function used to perform a repetitivecalculation. Defining a function is preferable toduplicating the expression every time it isneeded. Furthermore, a defun can be revised inone location.
Defparts are created by an ICAD user to representparts, assemblies or non-geometric entities. Thedefparts can be linked to each other throughdesign rules to form a cohesive model of a product.It is also possible to create functions in ICAD thatexpand the already large library of built-in func-tions. This is accomplished with defuns; oncedefined, a defun can be called just like anynormal function. The program is extensivelypowerful and extensive training is required toutilize its full potential.
Hand-vac choice attributesChoice attributes were incorporated to allow the
user to modify aspects of the model withoutreinstantiating it. These attributes allowed usersto enter new values into attribute fields or selectthem from lists. For example:
1. A battery size (AA, AAA, C, etc.) and chemicalcomposition (NiMH or NiCad) was chosenfrom a list of possible choices by the user.This was stored in the battery-size attribute.
The user's choice was matched to the appro-priate entry in an external data table and thecapacities, costs, dimensions, and weights ofthat selection were retrieved. If more flexibilitywas desired in the battery selection, entire tablesof manufacturer data can be used instead of thegeneric tables used in the model.
2. The `: run-time_min' was the run-time desiredin the model. The run-time was used to calcu-late the number of battery banks needed for aparticular size of battery. The total number ofbatteries was determined by the number ofbattery banks.
3. The `: intake-pressure_psi' was the desiredpressure at the nozzle intake. This pressurewas then used to calculate the intake area andthe length of the noz-intake part in the nozzle.
4. The thickness for the casing and nozzle wallswas determined by the `: casing-thickness_in'attribute. The overall mass and packaging con-cerns were affected by this attribute.
5. The length of the nozzle was derived from the`: nozzle-capacity_cu-in' attribute. The nozzlewas lengthened until the desired volume wasmet. As mentioned in the discussion of thefilter defpart, the desired volume was added tothe filter volume to achieve the true nozzlevolume.
6. The user was able to force ICAD to keep thebatteries in one group through the use of the`: force-unified-batteries?' attribute. By default,the batteries were put in the handle first andthen in the lower part of the case.
Hand-vac query attributesQuery attributes were used to read data from
tables. Query attributes can be used to retrieveentire tables or selected records from a table.Information regarding batteries from the catalog`batteries.table' was retrieved using the `: user-battery-selection' query-attribute.
A different table for each battery size or chemi-cal composition could be included in futureversions of the virtual prototype.
Hand-vac attributesNearly fifty attributes are incorporated in the
virtual prototype. The geometric and non-geometric attributes of the Black and Deckervacuum were established during decomposition,and the interrelated and changeable attributesalso identified, Fig. 8. Dimensions and otherinformation (densities, costs, etc.) passed on tothe defparts were represented by these attributes.Many of these attributes could have been includedin the defparts they define, but it was decided tocentralize as many attributes as possible. This wasdone to allow easier review of the outputs. Areview of the categories of attributes follows.
Information on the battery, motor, and fancosts, as well as the cost-per-weight of the casingmaterial, was provided by the costing attributes.This information was passed down to each part in
Table 1. Types of design rules used
Type Example
Calculation Force = Mass � AccelerationConditional Thickness = If (Pressure .30) 0.25 else
0.125Look-up database Battery-capacity = look up capacity of
selected battery from external fileFixed Filter-volume = 8.5 in3
Variable Number-of-batteries = any multiple of 3References Length of side A = (half(Length of side B))Execute external Thickness = FEA program resultprogramSelection Battery = battery that meets capacity,
the tree and then summed up in the `: total-cost'attribute.
The densities for the various parts in VirtualPrototype were provided by the `mass-property-info' attributes. These values were passed on totheir respective parts. The center-of-gravity loca-tion for virtual prototype was found by the `: CoG'attribute.
The dimensions of the battery case part wasdetermined by the battery casing attributes. Ifthere were no batteries in the battery case, it waseffectively removed. If batteries were present in thebattery case, a decision was made by the code todetermine whether one or two rows of batterieswere needed. A battery case height sufficient toclear the batteries was also determined.
Battery data such as length, diameter, anddensity were retrieved from the query attributesby the battery specs attributes. The number ofbatteries and the number of battery banks werealso determined by these attributes. The number ofbatteries increments in steps of 3, as three 1.2 Vcells were required to run the 3.6 V motor.
The behavior of the motor and fan was modeledby the fan/motor attributes. Specifically, the oper-ating current and intake pressure at maximumload were determined by these attributes. Thesevalues were used to calculate the number of banksneeded in the battery specs attributes.
Placement of the batteries in the virtual proto-type was achieved by the `battery-placement-routine' attribute. The total number of batterieswas split up into three separate values through asequence of conditional statements. Each valuewas passed on to the various battery case/batteryhandle parts in the virtual prototype. First, anattempt was made by the virtual prototype to fitthe batteries into the handle. If the handle lengthwas exceeded by the total length of the batteries,the batteries were also placed in the lower batterycase. If the `unify batteries' command was given toVirtual Prototype, the batteries were only posi-tioned in the batter case.
Lastly, a check by Virtual Prototype was run todetermine whether the batteries could be posi-tioned in an HCP arrangement within the battery
FANGeometric
Overall dimensionsBlade pitch
Non-geometricType
MaterialOperating speed
Airflow rateManufacture cost
Material costManufacture method
BATTERYGeometric
Overall dimensionsNon-geometric
Charge durationCharge time
VoltageChemical Composition
Max amperageNumber
Manufacture costMaterial cost
Manufacture methodWeightType
MOTORGeometric
Overall dimensionsNon-geometricPower Rating
Voltage RequirementAmp. Requirement
Useful LifeCost
WeightPotential manufacturer
InteriorComponents
NOZZLEGeometric
Overall dimensionsStorage volumeNon-geometric
Ease of attachmentAssembly method
Packaging constraints
CASINGGeometric
Overall dimensionsNon-geometric
MaterialManufacture method
Ergonomic ConstraintsPackaging Constraints
HAND-VACGeometric
Overall dimensionsNon-geometric
WeightCGCost
NorseControl layout
Assembly method
FILTERGeometric
Overall dimensionsMesh size
Non-geometricMaterial
Dirt StowageManufacture cost
Material costManufacture method
Exterior EnvelopeComponents
InteriorComponents
!
! !
!!
! !
! !
Fig. 8. Geometric and non-geometric attributes.
D. Calkins, et al.32
case. This determination was dependent onwhether the necessary handle and grip area wasinfringed upon by the battery case. The HCParrangement was discarded if it resulted in toolittle room for a user to grasp the handle.
Hand-vac partsVirtual Prototype was made up of nine parts; of
these, eight were actual parts present in the hand-vacuum. The ninth part was a marker-sphere thatindicates the center-of-gravity of the model. Null-parts were used to hide those parts that woulddisappear in a particular instance of the virtualprototype.
Hand-vac virtual design studyThree different hand-vac models were instan-
tiated using the Dirt-Hog ICAD virtual prototype.Each model is described below. HandVac (1),Fig. 9, has been specified to use size `C' NiMHbatteries. The handvac features a non-unifiedbattery packing arrangement, and due to thelarge size of the batteries, the four lower batteriesare restricted to a single row instead of two stackedrows. The remaining two batteries have beenplaced in the handle, as a C size battery's diameteris small enough to fit. The nozzle has been speci-fied to have a volume of 30 cubic inches. As can beseen, the Dirt Hog model has placed the batteriesin appropriate locations and sizes the nozzle toprovide the required capacity. The small sphere
present in the approximate center of the vacuum isa marker indicating the center of gravity.
HandVac (2), Fig. 10, is identical to the onedepicted in Fig. 9, except that the user forced thebattery arrangement to be unified. This results inDirt Hog placing all batteries in a single location.Very often, battery manufacturers will sell batterypacks in this form and the model has the flexibilityto reflect that possibility.
HandVac (3), Fig. 11, is also similar to theexample in Fig. 9. It differs in that it uses size `A'NiCad batteries. The lower energy density andreduced size result in a need for more batteries toachieve the same performance. However, due tothe smaller size of the selected battery, Dirt Hoghas decided to use an HCP arrangement for thelower seven batteries. This results in tighter pack-aging and a smaller vacuum than depicted in Figs 9and 10.
UNDERGRADUATE LEVEL HANDVIRTUAL PROTOTYPE DEVELOPMENT
The focus on the undergraduate version of theKBE class was to develop and model a parametrichuman being. The intent was to actually develop amodule of a KBE tool that will be used to design acomplete automobile [6]. The students createdboth the human form geometry and the governingdesign rules. The human was modeled based on an
ergonomic database developed by Dreyfus andAssociates [7, 8] and Diffrient, et al. [9]. Thedatabase contains human geometry for both maleand female gender and percentile (size). Percentilesincluded 2.5%, 50% and 97.5% for the entiredataset population.
The dimensions (lengths, widths and heights)were parameterized by dividing the dimensionsfrom the database by the human height. Thenon-dimensional human dimensions were plottedagainst the percentile for both men and womenand then curve-fitted to obtain the empirical designrules. Second-order polynomial fits were used forall design rules. All body parts were modeled inthis fashion to obtain the complete skeleton model.
For example the hand skeleton dimension, Fig. 12,is
Y � ÿ0:0015� 2̂� 0:007� 0:1054 �1�
The basic human form was modeled using acombination of spheres, cylinders and extrusionsthe approximate to the overall anatomical propor-tions. Each feature presents a given body part. Forexample, the head was modeled using an ellipsoidand tapered cylinders were used to approximatethe arms and legs with spheres at each joint. Thesebasic forms were positioned on what was called theskeleton, Fig. 13. The skeleton was used to posethe human model, in a seated position for example.
The 3-dimensional forms were then attached to theskeleton to form the complete human.
The skeleton geometry is shown in Fig. 14 alongwith the spherical joint location. The 3-dimen-sional forms are then added to form the completehuman. The human model requires three inputsto create the overall human geometry inProEngineerTM. These include:
1. Gender (male/female).2. Percentile, 2.5% to 97.5% (size).3. Seat type (posture geometry) e.g. sport utility
vehicle; sports car, coupe (two passenger);sedan (four passenger); coupe (four passenger).
The seat type specifies the seat posture geometryangles, Fig. 15. After specifying the desired humanand seat in TKSolver, the human dimensions arecalculated and sent to ProEngineer to generate thesolid model representation, Fig. 16. An example
instance of a 50th percentile male in a sports car isshown in Fig. 17.
CONCLUSIONS
The hand-vac virtual prototype was successfullydeveloped by the graduate students. A knowledgebase was developed for the motor, batteries, andfan. The hand-vac was then described throughgeometric and non-geometric attributes. Designrules were developed that incorporated these attri-butes and the knowledge base to allow construc-tion of a virtual prototype. The prototype wasprogrammed in the ICAD environment using thesolid-modeler. The prototype was designed toallow the user to alter certain key attributes inthe model to effect system-wide changes. Specifi-cally, the user could alter battery size and chemicalcomposition, battery packaging preferences,nozzle capacity, run time, and wall thickness ofthe casing. Based on these inputs, the prototypewould alter performance characteristics, geometryand outputs such as weight and cost. This allowedthe user to rapidly evaluate many different designsall sharing basic similarities.
The graduate class has completed its fourthoffering with several new and exciting develop-ments. The Boeing Co. actively supported theclass with the award of two Boeing student intern-ships for the summer 98 period. The class wasdivided into design teams of two students whoworked on the class product design project. Thefinal technical report and formal presentation atthe end of the quarter were judged by Boeingengineers, and the winning team received the twointernships. In addition, there were trips to theBoeing Co. to review their KBE projects, as well asin-class Boeing KBE specialists making presenta-tions. In addition because of Boeing's interest inthe class, the class has been opened up to graduatestudents in the departments of Civil and Aeronau-tical Engineering. Boeing has also requested thatthe class be opened up to senior undergraduate
Fig. 14. Parametric human (skeleton with solid forms).
Fig. 15. Seating position geometry.
D. Calkins, et al.36
engineering students to increase the number ofstudents exposed to the KBE technology. Afterlearning about the Boeing internships, GeneralMotors Truck and Pratt & Whitney each cameup with two additional internships to be awardedto class members.
The parametric human virtual prototype was
successfully developed by the graduate students.The PC/NT software suite chosen for the class wasalso successfully used. Because of the relative costsof the software and hardware. This approach willbe adopted in future classes. In addition, thecourse will be offered to both undergraduate andgraduate students simultaneously.
REFERENCES
1. W. J. Rasdorf, Perspectives on Knowledge in Engineering Design, Technical Report Article,Department of Civil Engineering and Computer Science, North Carolina State University, RaleighNorth Carolina. pp. 249±253.
2. D. E. Calkins, Learning all about knowledge based engineering, in Product Design and Development,Chilton Co., (September 1996), pp. 30±31.
3. C. L. Dym and R. E. Levitt, Knowledge Based Systems in Engineering, McGraw-Hill, Inc. (1991).4. A. J. Gonzalez and D. D. Dankel, The Engineering of Knowledge-based Systems: Theory and
Practice. Prentice-Hill (1993).5. M. R. Wagner, Understanding the ICAD_System, ICAD, Inc., Cambridge, MA (1999).6. D. Calkins, W. Chan, and W. Su, A design rule based tool for automobile systems design, SAE
International Congress, Detroit, MI (February 1998), TP 980397.7. H. Dreyfus, The Measure of Man: Human Factors in Design, 2nd Edition, New York, Whitney
Library of Design (1967).8. Dreyfus and Associates, The Measure of Man and Woman, New York, Whitney Library of Design
(1993).9. N. Diffrient, A. Tilley and D. Harman, Humanscale 7/8/9, MIT Press, Cambridge, Mass. (1974, 1981
and 1991).
Fig. 16. Combined wireframe skeleton, solid forms and seat.
Dale E. Calkins was an Associate Professor of Mechanical Engineering at the University ofWashington from 1979 until his death in 1999. He was a Lecturer at San Diego StateUniversity from 1977 to 1979 and a Visiting Professor at the Federal University of Rio deJaneiro, Brazil from 1976 to 1977. He worked as an engineer for Systems Exploration, Inc.,San Diego, CA (1977±79), the US Naval Undersea Center, San Diego, CA (1964±73) andThe Boeing Company, Seattle, WA (1961±64). His professional engineering career includedacademic, industrial, government and consulting experience in design engineering. Histechnical specialities included knowledge-based engineering, computer-aided design andengineering (CAD/CAE) and vehicle system design and analysis. He held the followingdegrees: D.Eng. in Naval Architecture from the University of California, Berkeley (1976);MS in Aerospace Engineering from San Diego State University (1969); and BS inAeronautical Engineering from the University of Detroit (1961). He was a RegisteredProfessional Engineer in the State of Washington. Professor Calkins passed away onTuesday, June 29, 1999. He was 61 years old at the time of his death.
Christian Scholz is an engineer with Sandia National Laboratories, in Livermore, CA. Hereceived his BSME in 1997 from the University of Oklahoma, in Norman, OK. His MSMEresearch at the University of Washington focused on the development of a prototypknowledge-based engineering automotive development program. During his graduateeducation, he was an instructor in the densior mechanical engineering capstone course,where he guided students in the design of a small Formula-style race cars for entry into theSAE Formula student design competition. He has participated in the development of fivesuch vehicles, from 1995 through 1999.
