KDI: Networked Engineering
A Joint Research Initiative of
CMU-Drexel-USC
William C. Regli
Assistant Professor and Director
Geometric and Intelligent Computing Laboratory
Department of Mathematics and Computer Science
Drexel University
http://gicl.mcs.drexel.edu
KDI:NE Project Goals
To develop,
integrate and
evaluate
information systems
for distributed
and
collaborative design
and
manufacturing.
Project Overview
• NSF Grant:
• Program:
• Amount:
• Duration:
CISE/IIS-9873005
Drexel University
NSF Knowledge and Distributed
Intelligence (KDI) Initiative
$1.2M
October 1998 -- October 2001
One of 40 projects selected in 1998 (of 697 proposed)
Principal Investigators
• Carnegie Mellon University
– Pradeep Khosla
– Ramayya Krishnan
• University of Southern California
– Berok Khoshnevis
– Stephen Lu
• Drexel University
– Thomas Hewett
– William Regli
Industry and Gov’t Partners
• AT&T Labs, Internet Platforms
• National Institute of
Standards and Technology
• Structural Dynamics
Research Corporation (SDRC)
• Delaware Valley Industrial
Resource Center (DVIRC)
• Bridgeport Machine Tools
• Ford Motor Company
KDI: Networked Engineering
• Communication of Information
– within an office
– across virtual organizations
– to suppliers and customers
Information
• Access to Services
– human expertise
– software agents
Services
Collaboration
• Collaboration & Negotiation
– among different disciplines and departments
KDI:NE Research Objectives
• Design Repositories
– Engineering knowledge-bases to leverage legacy knowledge
• Composable Simulation
– CAD enhanced with engineering analysis and behavior models
• Collaborative Negotiation
– Conflict management strategies for design
• Usability Evaluation
– Assess computational support for collaborative design
• Networked Engineering Studios
– Integrate Internet, collaboration, and CAD tools
Design Repositories
Goals:
Record, archive and manage design information as it is created
during distributed design activities.
Approach:
– Message model for distributed design
– Process archival methodologies to populate design knowledge-bases
– Retrieval strategies for design knowledge-bases
Impact:
– Enables variational design
– Access and reuse of legacy data and information
– Platform for networked collaboration on and knowledge sharing about
design problems
Related Project: NSF CAREER Award CISE/IIS-9733545
Repository Scenario
Engineering Digital Libraries
 s  M(θ)  θ  V(θ, θ )  G(θ)  F(θ, θ )
… contain CAD models, assemblies, plans,
revisions, S-B-F models, project information and
workflows, design rationale...
Research Objectives
• Integrated engineering knowledge-bases and
engineering digital libraries
• Intelligent decision support tools for design
• Techniques to leverage legacy knowledge
Current Results and Accomplishments:
• Graph-based structures for knowledge modeling
• Geometric search algorithms
• Collaborative/Conceptual interfaces
• Internet-Based Design Repository
National Design Repository
http://www.parts.nist.gov
http://repos.mcs.drexel.edu
• Enables national and
international participation
• Links in related resources
• To be coupled
with intelligent search
and retrieve tools
Involved Drexel/GICL
Research Personnel
•
•
•
•
•
•
Dr. William C. Regli (MCS) • NSF REUs
– Lisa Anthony
Dr. Thomas Hewett (PSA)
– Dmitry Genzel
Dr. Wei Sun (Mech. Eng.)
– J. Elvis John
Dr. Jon Sevy (GICL Asoc. Dir.)
– David McWherter
Mr. Gaylord Holder
– Yuriy Shapirshteyn
GRAs
– Joshua Wharton
– Vincent Cicirello (MS, 1999)
• Part-Time & Workstudy
–
–
–
–
Xiaochun Hu (PhD)
Max Peysakhov (MS, 2000)
Xiaoli Qin (MS, 1999)
Vera Zaychik (MS, 2000)
– Binh Le
– Victoria Charles
Current Status
• Deploying Repository site
• Initial implementation of
– Conceptual query interface
– Structure matcher
• Data acquisition
– CAD data (w/ SDRC, PTC, NIST)
– Process/Assembly Plans (w/ Bridgeport and CMU)
• Integrated with fabrication services
– GICL’s Bridgeport 4-axis machining center
Future Work
• Integrate Repository with K-base system
• Approximation algorithms for
– structure matching
– distance measurement
• Enhanced design graph representation
• Experimentation and testing of conceptual design/query
interface
• Adaptable query interface for Internet agents
• Integrated cost estimation, planning, and manufacturing
network services
Composable Simulation
Goals:
Create simulations of mechatronic systems by composing
mechanical CAD models, electrical models and information
technology.
Approach:
–
–
–
–
Automatically create product-level simulations
CAD enhanced with analysis and behavior models
Hierarchical distributed simulation architecture
CORBA-based implementation
Impact:
– Allow reuse of simulation models
– Significantly reduce the time to build simulators
– Increase fidelity of simulations
Scenario: Conceptual Design...
p
p
Pitch
s
m
Control
Signal
Reference
PID
 s  K s ( m   p )
 m    J m  bm     s
Coupling
Pitch Motor
 s  M(θ)  θ  V(θ, θ )  G(θ)  F(θ, θ )
PID
y
s
m
Control
Signal
Reference
Mechanical
System
 s  K s ( m   y )
 m    J m  bm     s
Yaw Motor
Coupling
Yaw
y
… to Model Simulation
Design concept
CAD and
Virtual prototyping
Ref.
Control
Yaw
motor
Dynamics
Control
Pitch
motor
Automatically
generate
dynamic model and
simulation software.
Ref.
System Overview
Conceptual
Design
Component
Models
Simulation software
architecture
Linpack
Simulation
processes
Odepack
Matlab
Information
Agents
Dymola
ACIS
Novel Features
• Creation of simulation software by
combining individual simulation
processes
• Inclusion of information agents in
simulation process
• Provision of distributed environment
• Automatic model refinement
Component Models
• Object-Oriented Modeling Paradigm
– reuse of models
• Design Repository used to select CAD
components
– incorporates ADAMS or DADS
• Information Agents
– control system algorithms
– environment definition
Software Architecture
• Analyze conceptual graph to create
simulation processes
– distributed objects
– retrieve CAD information via ACIS
• Build simulator architecture
– synchronization mechanisms
– communication protocols
• Execute simulation
Simulation Output
Collaborative Negotiation
Goal:
Develop systematic methods to establish optimal strategies to guide
design team interactions and to manage design conflicts raised from
these interactions.
• Approach:
– Game-theoretic modeling of collaborative design activities
– Establishment of conflict management strategies for mechatronic design
problems
•
Impact:
– Theoretical foundations for new software tools to support collaboration and
negotiation activities
– Techniques for trade-off analysis in mechatronic systems design
•
Related Projects:
– DARPA/CMU CODES
– CMU DecisionNet
Outline
• The problem context
– enterprise-wide decision support for military logistics
planning
• The approach
– use an e-commerce metaphor to create a virtual repository
of decision support resources
• The research challenges
– metadata (what kinds, representation..)
– the search and discovery problem
Overview of DecisionNet
•
Architecture
– Providers/developers of decision support objects
• register with broker
• provide metadata
– Broker(s)
• compiles metadata into a catalog
• supports search to respond to consumer queries with varying
degrees sophistication
• returns executable plans (an ordered collection of services)
– Users
• use broker to search and retrieve resources/computational
plans
• use broker to execute resources to solve problem
• computational objects in the repository
Networked Engineering Studios
Goal:
Deploy testbed of Design Studios that integrate Internet technologies
and collaboration/multimedia tools with proactive CAD systems and
inter-networked engineering and fabrication services.
• Approach:
– Leverage vBNS, COTS software and strong industry collaboration
– Merge collaborative work tools with tools for design, manufacturing,
negotiation and Product Data Management (PDM)
• Impact:
– Interdisciplinary learning/work environment: CS/EE/ME/Psyc/CE/IE/HCI
– New classroom for industry
– Platform for evaluation
Initial Sites
vBNS Logical Network Map
75 Operational Connections
19 Planned Connections
Last Updated 2/1/99
Wisconsin @
Madison
Wisconsin
@
Milwaukee
FNAL
UIC
ANL
Chicago
Northwestern
Dartmouth
Notre Dame
Minnesota
Iowa State
UNH
Brown
Boston U
Purdue
Washingto
n
MIT
Harvard
Indiana
Iowa
MREN/
STARTAP
NREN
PNW
DREN
ESnet
Rensselaer
Rochester
CMU
Merit
Penn State
Michigan
UC Boulder
PSC
Michigan State
NREN
Boston
Syracuse
NYSERNET
Cornell
Ohio State
NASA
AMES
NCAR
UNM
SUNY Buffalo
Wayne State
DREN
Seattle
Yale
SREN
NI
Oregon State
UMass
CA*Net
II
APAN
ESnet
NYU
Rutgers
Utah
Chicago
Columbia
Cleveland
Princeton
New York City
UC Davis
DREN
UC Berkeley
Denver
UCSF
NCSA
Missouri
UIUC
Stanford
CalREN-2
North
UCSC
iDREN
Sprint
NY NAP
Washington @
St. Louis
Drexel
UPenn
Perryman, MD
Johns Hopkins
San Francisco
UMBC
UMD
UCLA
CalTech
UCSB
USC
Washington DC
Cal Poly
Pomona
SDSC
UC Irvine
UC Riverside
Cal State
San Bernardino
Kentucky
vBNS Approved
Institution
Planned vBNS
Approved Institution
vBNS Partner
Institution
Network of vBNS
Partner Institutions
 1998
Planned Network
of vBNS Partner
Institutions
Texas
Aggregation Point
Planned
Aggregation Point
DS3
OC3
OC12
DREN
NIH
NCSC
Vanderbilt
SDSU
ESnet
iDREN
MCI - vBNS POP
Wake Forest
UCSD
NI
MCI Reston
Houston
Arizona
MAX
UNC
UT Austin
Highway 1
Alabama @
Birmingham
Duke
SoX
VA Tech
ODU
Baylor C.
of Medicine
IB&T @ Houston
Houston
Rice
TAMU
MFS
DC NAP
NREN
Atlanta
CalREN-2
South
USC ISI
ESnet
Los Angeles
GA
State
NC State
GA Tech
UVA
George Washington
Florida
FSU
Miami
Georgetown
Central Florida
NOTE: Lines between institutions and aggregation points or NAPs represent the configured bandwidth of their connection to the vBNS.
The bandwidth of the actual circuits may be greater than shown.
Evaluation & Human Factors
Goal:
Work with practicing engineers and engineering educators to
improve support for design and to understand performance of
designers in distributed and collaborative design environments.
Approach:
• Assess computing system support for design and collaborative
design through empirical examination of interaction effects among:
– Hardware and Software characteristics
– Identifiable sets of users
• Evaluation as part of computing system design process:
–
–
–
–
Provides feedback to designers
Enables users to contribute tool and system design ideas
Forces ongoing concern with goals and the criteria for meeting them
Evaluation will happen
Impact
• Improved tools for engineering design and collaboration
• Tools that designers will want to use
• Assessment techniques
Collaborators:
–
–
–
–
–
–
DVIRC
Drexel ME
SDRC
Bridgeport
Ford
NIST
Assembly Structures
CUP: Conceptual
Understanding and Prototyping
• Functional requirements
“Enable back-of-the-envelope sketching”
– capture basic 3D assembly structure
– part relationships
– function characteristics
– behavior characteristics
• Collaborative environment
• Internet-Centric