simulation, exploration, and understanding in engineering g. w. rubloff materials science &...
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Simulation, Exploration, and Understanding in Engineering
G. W. RubloffMaterials Science & Engineering, and Institute for Systems Research
University of [email protected]
www.isr.umd.edu/~rubloff/
How can we help people develop insight in both engineering education and practice ?
How can we help people develop insight in both engineering education and practice ?
Center for Engineered Learning Systemswww.isr.umd.edu/CELS/
Institute for Systems Research
Human-Computer Interaction Laboratorywww.cs.umd.edu/hcil/
Institute for Advanced Computer Studies
with special thanks toAnne Rose, HCIL
Developing Insight in Engineering Education and Practice
CHALLENGESDomains are unfamiliar to the user
Often no hands-on physical experience
Unfamiliar length and time scales
Principles are abstractSubtle until experienced
Ultimately must be understood in mathematical terms
Systems-level behavior enlarges complexity
Multi-level metrics
Heterogeneous, hierarchical models
Dynamic & stochastic behavior
Environments and tools for engineering insight are limited
Education and training
Broad engineering practice
EXAMPLE: semiconductor chips
DepartLeave
Pick StationArrive Stocker
materials & processes
transistors & chips
people
factory costs and operations/ logistics
equipment
Developing Insight in Engineering Education and Practice
CHALLENGESDomains are unfamiliar to the user
Often no hands-on physical experience
Unfamiliar length and time scales
Principles are abstractSubtle until experienced
Ultimately must be understood in mathematical terms
Systems-level behavior enlarges complexity
Multi-level metrics
Heterogeneous, hierarchical models
Dynamic & stochastic behavior
Environments and tools for engineering insight are limited
Education and training
Broad engineering practice
SOLUTIONS
Simulations ofphysical phenomena
Desired attributes of simulation environments
materials &processes
circuits& chips
equipment
factoryoperations& logistics
transistor devices
cost ofownership
Engineering SimulationsEXAMPLE: semiconductor chips
dynamic/stochastic discrete event
static spreadsheet
finite element
dynamic continuous parameter
Monte Carlo
materials &processes
circuits& chips
equipment
factoryoperations& logistics
transistor devices
cost ofownership
Engineering SimulationsEXAMPLE: semiconductor chips
dynamic/stochastic discrete event
static spreadsheet
finite element
dynamic continuous parameter
Monte Carlo
While valuable to specific technical experts,how beneficial are these for
education and broader practice?
Desired attributes of simulation environments
Developing Insight in Engineering Education and Practice
CHALLENGESDomains are unfamiliar to the user
Often no hands-on physical experience
Unfamiliar length and time scales
Principles are abstractSubtle until experienced
Ultimately must be understood in mathematical terms
Systems-level behavior enlarges complexity
Multi-level metrics
Heterogeneous, hierarchical, dynamic, stochastic behaviors
Environments and tools for engineering insight are limited
Education and training
Engineering practice
SOLUTIONS
Simulations ofphysical phenomena
Self-directed and guided hands-on experiences
Connectivity tounderlying fundamentals
Complexity management throughIntegrated, heterogeneous simulations
Tools to help the userdevelop understanding and insight
Separable authoring andrapid module development
SimPLESimPLE
timer
keep history
communicate
access background
and guidance materials, locally or
from Internet
save & document
control thesimulation view
dynamic results
carry out experiments and annotate
results
operate system and see consequences
in real time
Demos in HCIL
Demos in HCIL
Simulated Processes in a Learning Environment
Features in the SimPLE SimPLE Framework
Design ofexperiments
Simulation control at system image
Guidance – local & Internet
Assignedexercises
Conditionwatchdog
E-mail tool
Learninghistorian
Labnotebook
Timer
Systemdesign
configurator
Processrecipes
Graphs &charts
Visualizationcontrol
Tightly-coupledguidance
Changemodule
learnerTeacher kit Authoring
in html
teacher
Domain-specificDelphi objects
Domain-specificsimulation models
and submodels
SimPLEframework
Separableauthoring
author / developer
Tightly-Coupled Guidance
Learning Historian1. Do a simulation
3. Replay the simulation history
2. Record and save the simulation history
4. Review, revise, & annotate the history
5. Share the history with peers & instructor
Simulation
History
Configurationsetups
Configurationsetups
Teacher Kit
System design parametersSystem design parameters
GUI componentsGUI components
Guidance materialsGuidance materials
Historian configuration
Error messagesSimulation models
Teacher may create specific setups to customize educational scaffolding
Teacher may create specific setups to customize educational scaffolding
SimPLE Applications
WaterSimWaterSimenvironment &
manufacturing
NileSimNileSimhydrology &
social science
Processrecipe
Processrecipe
Factorysimulation
Factorysimulation
Cluster toolscheduling
Cluster toolscheduling
Sensitivityanalysis
Sensitivityanalysis
Cluster toolconfiguration
Cluster toolconfiguration
HSEHSEfactory
operations
fail
fail
pass
Oxide growthtemperature
Oxide thickness
Capacitorarea
Capacitance
YIELD
SortSimSortSimcomputing
algorithms
EquiPSimEquiPSimsemiconductor
manufacturing
TrafficSimTrafficSimtransportation
management
WaferMapWaferMapmultistep process
optimization
Messages
Engineering insight through SimPLE environmentsFree and guided exploration through simulationPowerful tools for individual and collaborative learningAlso: science, computer science, math, social science, …
You can use this learning systems technology nowTeachers – specific topical areas & development of new areasDevelopers – SimPLE platform & new features to come
We invite your participationCollaborations, workshops, …
www.isr.umd.edu/CELS/
Acknowledgements
ENGINEERING
L. Henn-Lecordier
B. Levy
P. Tarnoff
G. B. Baecher
B. Levine
J. W. Herrmann
Simulation software platformCommercial applications& customization
Research support
CEBSMResearch partnership
for semiconductor ESH
Research partnership for tech training
COMP SCI & UMIACS
A. RoseA. RoseB. Shneiderman
C. Plaisant
G. Chipman
EXTERNAL
F. Shadman (U. Arizona CEBSM)
M. Lesiecki (MATEC)
S. Braxton (Bowie State)