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Simulation, Exploration, and
Understanding in Engineering
G. W. Rubloff
Materials Science & Engineering, and Institute for Systems Research
University of Maryland
[email protected]
www.isr.umd.edu/~rubloff/
How can we help people develop insight in
both engineering education and practice ?
with special thanks to
Anne Rose, HCIL
Center for Engineered Learning Systems
www.isr.umd.edu/CELS/
Institute for Systems Research
Human-Computer Interaction Laboratory
www.cs.umd.edu/hcil/
Institute for Advanced Computer Studies
Developing Insight in Engineering
Education and Practice
CHALLENGES
EXAMPLE:
Domains are unfamiliar to the user
materials &
processes
Often no hands-on physical
experience
Unfamiliar length and time scales
semiconductor chips
transistors & chips
Principles are abstract
Subtle until experienced
Ultimately must be understood in
mathematical terms
people
equipment
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
factory costs and
operations/ logistics
Arrive
Pick
Station
Leave
Stocker
Depart
Developing Insight in Engineering
Education and Practice
CHALLENGES
SOLUTIONS
Domains are unfamiliar to the user
Often no hands-on physical
experience
Unfamiliar length and time scales
Simulations of
physical phenomena
Principles are abstract
Subtle 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
Desired attributes of simulation environments
Engineering Simulations
EXAMPLE:
semiconductor chips
Monte Carlo
materials &
processes
transistor
devices
finite element
equipment
circuits
& chips
dynamic continuous parameter
factory
operations
& logistics
static spreadsheet
cost of
ownership
dynamic/stochastic discrete event
Engineering Simulations
EXAMPLE:
semiconductor chips
Monte Carlo
materials &
processes
transistor
devices
finite element
While valuable to specific technical experts,
how beneficial are these for
education and broader practice?
equipment
circuits
& chips
dynamic continuous parameter
factory
operations
& logistics
static spreadsheet
cost of
ownership
dynamic/stochastic discrete event
Developing Insight in Engineering
Education and Practice
CHALLENGES
SOLUTIONS
Domains are unfamiliar to the user
Often no hands-on physical
experience
Unfamiliar length and time scales
Simulations of
physical phenomena
Principles are abstract
Subtle 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
Self-directed and guided
hands-on experiences
Tools to help the user
develop understanding and insight
Connectivity to
underlying fundamentals
Complexity management through
Integrated, heterogeneous simulations
Separable authoring and
rapid module development
Desired attributes of simulation environments
SimPLE
Simulated Processes in
a Learning Environment
control the
simulation
view
dynamic
results
keep history
timer
communicate
save & document
access
background
and guidance
materials,
locally or
from Internet
operate system and
see consequences
in real time
carry out
experiments
and annotate
results
Demos
in HCIL
Features in the SimPLE Framework
Simulation control
at system image
Tightly-coupled
guidance
Assigned
exercises
Condition
watchdog
Change
module
Lab
notebook
System
design
configurator
Learning
historian
Timer
Process
recipes
Design of
experiments
E-mail tool
Graphs &
charts
learner
Teacher kit
teacher
SimPLE
framework
author / developer
Guidance –
local & Internet
Visualization
control
Authoring
in html
Separable
authoring
Domain-specific
Delphi objects
Domain-specific
simulation models
and submodels
Tightly-Coupled Guidance
Learning Historian
1. Do a simulation
2. Record and save the simulation history
3. Replay the simulation history
4. Review, revise, & annotate the history
5. Share the history with peers & instructor
History
Simulation
Teacher Kit
Guidance materials
Configuration
setups
Simulation models
Error messages
Historian configuration
Teacher may create specific
setups to customize
educational scaffolding
GUI components
System design parameters
SimPLE Applications
TrafficSim
transportation
management
SortSim
computing
algorithms
NileSim
EquiPSim
hydrology &
social science
WaferMap
semiconductor
manufacturing
Oxide growth
temperature
Oxide
thickness
multistep process
optimization
Cluster tool
scheduling
YIELD
Process
recipe
fail
Capacitor
area
Capacitance
fail
Sensitivity
analysis
WaterSim
environment &
manufacturing
Cluster tool
configuration
HSE
Factory
simulation
factory
operation
s
Messages
Engineering insight through SimPLE environments
Free and guided exploration through simulation
Powerful tools for individual and collaborative learning
Also: science, computer science, math, social science, …
You can use this learning systems technology now
Teachers – specific topical areas & development of new areas
Developers – SimPLE platform & new features to come
We invite your participation
Collaborations, workshops, …
www.isr.umd.edu/CELS/
Acknowledgements
ENGINEERING
L. Henn-Lecordier
B. Levy
P. Tarnoff
G. B. Baecher
B. Levine
J. W. Herrmann
COMP SCI & UMIACS
A. Rose
B. Shneiderman
C. Plaisant
G. Chipman
CEBSM
Research support
Commercial applications
& customization
Research partnership
for semiconductor ESH
Simulation software platform
EXTERNAL
F. Shadman (U. Arizona CEBSM)
M. Lesiecki (MATEC)
S. Braxton (Bowie State)
Research partnership
for tech training