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CYBER-INFRASTRUCTURE SUPPORT FOR ENGINEERING DESIGN

Perspectives from NSF ED2030 Workshop + +

Jami J. Shah

Mechanical & Aerospace Engineering,Arizona State University, Tempe

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Industry representationBoeing Commercial, Boeing Defense, Intel, General Motors, John Deere, Ford, HP, Dassault, CPD Associates, IMTI

Government agenciesNSF, NASA, NIST

Disciplines representedMechanical, Aerospace, Civil, Industrial, EE, Computer Science, Chemical, Materials, Bio, Industrial Design

Participants

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Pre-conference– position papers posted on-line– on-line chat for discussion and comments

Plenary sessions– understand the scope & objectives– review current state of eng. design– industry and technology forecasts

Workshop Organization

Breakout sessions– four focus groups, working in progressive stages:

• Ideation & design innovation• Theoretical foundations of design• Social aspects of design• IT & computational tools for design

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Forecasts were delivered for the following sectors:

- Aviation industry (Boeing)- Space industry (NASA JPL)- Automotive industry (GM)- Consumer electronics (HP)- CAD/CAM/PLM (CPD Associates)- Virtual prototyping (John Deere)

Industry was reluctant to provide predictions beyond 10 years (except for space industry)

Details can be found at:http://dal.asu.edu/engdesign/index.html

Industry forecasts

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• New developments in biology, bio-engineering, computers, etc. will directly affect what we design (products) and how we design (process, tools)

• Future products will be typified by complex structure and function but simpler usage (interface).

• Consumer products will be customized for each user• customers will have direct involvement in product design• Computer models will be embedded in delivered products for customization

and “soft” reconfiguration• Design teams, manufacturing resources and customers will be globally

distributed • Designs will be constrained by new and stricter regulations related to:

emissions, noise, recycling, energy, global warming, terrorism…• Designs will be driven by public demand for: higher reliability, better safety• Designs will be driven by the desire to seek products least vulnerable to

international competition • New markets in developing countries will offer new opportunities but

require consideration of greater cultural and economic diversity• Design services will also be outsourced overseas; US companies will do

system of system integration

Industry forecasts: common themes

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Vision of Computing Environments for ED 2030

•In 2030 designers, distributed across disciplinary, geographical and temporal boundaries, will work synergistically within design environments that– enable designers to focus on design issues without being distracted by the

underlying computing infrastructure– encompass all stages of the product life cycle– operate at multiple levels of abstraction– enhance the creativity of design teams leading towards innovation-guided

design– are capable of spawning tasks transparently; automatically refining designs or

providing support, executing integrated multi-scale simulations/analyses – facilitate communications with other human designers, web services,

repositories, catalogs, and digital libraries as needed. – enable the generation of solutions based on relationships among the design

requirements and their physical actualization and context of use– interact with designers in human terms and language; no distinction in human-

human, human-computer, computer-computer communications.• Future design tools and methods will not only support analysis and decision

making from a technological point of view, but will also account for psychological, sociological, and anthropological factors based on fundamental understanding of these factors and their interaction.

• Boundaries between resources, people, and information/knowledge will disappear.

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Major Recommendations at a glance

• Engineering innovation: Create a Division level initiative in developing the Science of engineering innovation directed at a fundamental understanding of the nature of engineering innovation to create new technologies, products, processes and services.

• Social-technical aspects: Create an agency wide, multi-disciplinary program in the socio-technical aspects of engineering design; Basic knowledge regarding how humans and social dynamics influence product specifications, design process and decisions with multiple stakeholders.

• Design informatics: Focus the current Engineering Design Program on critical research areas listed above and theoretically founded design modeling, simulation and automation environments that represent functional intent, design choices, prediction of behavior.

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Why invest in Cyber-infrastructure for Eng. Design?• Engineering design is a discovery process that requires:

– exploration to develop a clear understanding of the problem,– creativity and innovative skills to synthesize and predict the future

outcome of a design.– Experiential and analytical knowledge, methods, tools to predict and

optimize the performance of a design– past experience and access to past designs, catalogs…to re-use past

designs or to avoid past mistakes• But

– The knowledge used during design (rationale) is currently not captured in any consistent form that it can be reused other then by the person who recorded it, if any records were kept at all

– Due to time and budgetary constraints exploration of design alternatives is often curtailed

– Poor integration of design & analysis tools and between disciplines, less time spent on optimization

As products become more complex, traditional engineering methods, islands of automation and trial & error will not be sufficient

Need computing infrastructure, computational tools and knowledge bases for Predictive Product Realization

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Predictive Product Realization

• Progression of design and manufacturing towards predictive product realization needs to take place along two directions: enhancing our state of knowledge and increasing the level of automation.

• Better understanding of physical phenomenon will reduce trial and error in product realization.

• Increasing the level of automation will allow more alternatives to explored and more simulations to be carried out in a given amount of time.

• Will enable predicting the desired performance in future products at known cost.

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ENGINEERING DESIGN GENERIC PROCESS

PROBLEM DEFINITIONDesign Objectives

Tech specifications, QFD CONCEPTUAL DESIGNData collection, research

Functional analysisConcept generation

Concept evaluation, selection

EMBODIMENT DESIGNIdentification of key parameters

Material selectionGeometric & parametric design

Engineering analysesTrade-off studies

Economic analysis; Optimization

DETAILED DESIGN

Detailed layoutCAD models, eng. drawings

Tolerance analysisDfXPROTOTYPING

& TESTING

• Very little computational support for CAD tools for conceptual design• Design history, rationale poorly archived• Poor inter-operability between tools for embodiment and detailed design

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The CAD Technology Gap

support for capturing design intent, rationale, history

Geometric model construction history

Multi-modal solversGeometric constraint solvers

Pro-activeReactive, iterativeCustomizable to domain, Company, designer

Limited customizability

Design RepositoriesStd. part geometry & features catalogsKnowledge drivenUser driven

Generative + variantGenerative designShared useDisconnected use

Conceptual & functional DesignEmbodiment designPhysicsGeometryWANTHAVE

•CAD companies have very little R&D capacity•CAD systems have always operated on Technology PUSH•Most critical information is not archived,or not in digital format

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The CAE/Simulation Technology Gap

Analytical + experientialAnalytically based CAE tools

Multiple fidelity modelsHi-fidelity modelParallel processSerial process

Mesh-less analysis or intelligent tools to automate pre-process

Long cycle times, labor intensive

Usable by designersRequires specialists in the loop

StochasticDeterministic

WANTHAVE

• Discipline specific analyses, conducted by specialists• Each CAE tool requires different type of input• Enormous amount of time spent in model preparation

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The PLM/EIM Technology Gap

PDM systems must manage data inside the files

PDM systems manage files

Standards need to be extended to engineering analyses

Limited inter-operability between CAE systems

Multi level (adjustable) level of granularity

Coarse level of granularity; data exchange limited to low level data

Multi-domain constraint management, propagation mechanism

No constraints, relations between data in different applications

Complete exchange of all product data

Not all CAD data can be exchanged today

Shared databases, knowledge bases

Physical file exchange

WANTHAVE

• Typically, companies have focused on managing “Released” designs • Most design information is not even in digital format

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Design Informatics: ResearchEngineering informatics:

flexible, extensible ontologies for devices, functions, behavior, embodiment mechanisms to inter-relate ontologiesinformation exchange standards information management at meaningful levels of granularitybuilding blocks & standardized APIs to repositories and libraries

Models to support Smart tools: knowledge capture/re-use, smart search enginesKnowledge Representation for Design Synthesis

“fast” approximate models to support preliminarydesign exploration tools for analysis with incomplete information/uncertaintyConjoint exploration of the Requirement Space and Solution SpaceMulti-scale models for shallow and-or deep granular searchingSophisticated knowledge representation structures to bridge CAD. CAE, AI

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Design Informatics: Research - II

Collaboration infrastructuremulti-disciplinary synthesis and distributed

collaborationorganizational modeling; agents architectureintelligent constraint management, conflict

resolutiondevelopment and integration of multi-physics

simulation tools with design synthesis tools for Virtual Prototyping

Design Decision Support: Ability to make decisions at all phases of the design process: varying levels of abstraction, information completeness and uncertaintyMethods appropriate for distributed, progressive, iterative processes with multiple decision makers and multiple disciplines

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ED2030 relation to Atkins ReportAtkins Report identified five types of services that are also relevant to ED: • High performance computation services:

– Needed for multi-physics simulation, • Data, info, knowledge management services:

– Design repositories with associated search engines for finding past similar designs

– Digital libraries for technical documents, books, design procedures, tutorials, algorithms

– Catalogs of standard parts, devices, materials for sale– Design history, evolution archival– Advanced PLM/EIM systems for

• Measurement, fabrication services: needed for product realization• Interfaces, visualization services:

– Advanced design environments; immersive environements; exchange stds.• Collaboration services

– Analysis/Consulting services for specialized engineering analyses– Collaboration tools; infrastructure

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Research ChallengesHow can we create tools (and representations) that:• adaptively represent relevant physics at appropriate scale?• integrate multi-domain knowledge in design libraries capable of

supporting single and group users?• help designers understand and manage design complexity?• integrate the science and art of design• have components that are interoperable and “plug-able”

• deliver the information at the right level of abstraction for all design activities?

• capture evolution and rationale of design?• integrate analytical & experiential knowledge• enable the access, reuse and adaptation of prior designs and processes?• can represent a variety of types of requirements (e.g., qualitative and

quantitative)?• enable methods for discovering, representing, capturing knowledge?• provide multiple views of information based on who is using it and

when it is used?

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Acknowledgements

• This report has freely used material from attendees of ED2030, particularly the Focus Group Leaders:

• Supported by NSF DMII-0411591• Website: http://dal.asu.edu/engdesign/index.html• E-mail: jami.shah@asu.edu

Paul Wright, UC Berkeley Larry Gutkowski, HPLarry Leifer, StanfordStephen Lu, USC David Trees, John DeereCarolina Cruz, Iowa State J. Vandenbrande, Boeing Co.Jon Cagan, CMU Joe Dondelinger, General MotorsSusan Finger, Carnegie Mellon