Modeling, Simulation and Implementation using MATLAB · PDF fileModeling, Simulation and...
Transcript of Modeling, Simulation and Implementation using MATLAB · PDF fileModeling, Simulation and...
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1© 2016 The MathWorks, Inc.
Modeling, Simulation and Implementation using
MATLAB & Simulink
Dhirendra Singh
Application Engineer, MathWorks
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Agenda – Day 1Day 1
09:00 am - 09:30 am Registration
09:30 am - 09:45 am Welcome Address
09:45 am -11:00 am
MATLAB and Simulink in Engineering Education
MATLAB Basics for the Budding Engineer
•Experimentation and Modeling in MATLAB
•Design and Implementation
•Project-Based Learning (Arduino, RaspberryPi)
•Machine Learning & Data Analytics
11:00 am - 11:30 am Tea Break
11:30 am - 12:30 pm
•Accessing, exploring, analyzing, and visualizing data in MATLAB
•Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform
•Common machine learning tasks such as Feature selection and feature transformation
•Demo: ADAS using live camera stream
12:30 pm - 2:30 pm Lunch Break
02:30 pm - 04:30 pm
•Electrical engineering concepts Using MATLAB and Simulink
•System Identification & Neural Network Based System Modeling Techniques
•Electrical engineering using Simscape (Physical Modeling)
•Electrical engineering using SimPowersystems
•Control system design and analysis
•What is IoT?
• Market Drivers and Challenges
• Introduction to ThingSpeak
• Examples
• Other IoT examples using MW tools
• MATLAB and Simulink Capabilities for IoT
04:30 pm - 05:00 pm Tea Break
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Agenda – Day 2
Day 2
10:00 am -11:00 pm
•Mechanical engineering and Control System Concepts Using MATLAB
and Simulink.
Mechanical engineering concepts using Simscape (Physical Modeling)
•Multi-Body Dynamics Simulation using SimMechanics
11:00 am - 11:30 am Tea Break
11:30 am - 01:00 pm
•Import CAD Models using GetMechanics App
•Implement Control on Low cost hardware - Arduino
•Demo of Magnetic Levitation System
1:00 pm - 2:00 pm Lunch Break
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Key Industries
Aerospace and Defense
Automotive
Biotech and Pharmaceutical
Communications
Education
Electronics and Semiconductors
Energy Production
Financial Services
Industrial Automation and
Machinery
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July 2014 News
IEEE Spectrum used 12 different metrics to compare
programming language popularity and use.
Data sources include Google CareerBuilder, and
Github.
The Right Language for the Task
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Supporting Innovation
MATLAB Central
Open exchange for the
MATLAB and Simulink user community
800,000 visits per month– 50% increase over previous year
File Exchange– Free file upload/download, including
MATLAB code, Simulink models, and
documents
– File ratings and comments
– Over 9,000 contributed files, 400 submissions
per month, 25,500 downloads per day
Newsgroup and Web Forum– Technical discussions about
MATLAB and Simulink
– 200 posts per day
Blogs– Read posts from key MathWorks developers
who design and build the products
Based on Feb-March 2009 data
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Classroom Resources at mathworks.in
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Teacher Activities
Before a course
• Define learning objectives
• Find or write material
• Decide about teaching forms
• Decide about examination
During a course
• Lectures
• Practical sessions
• Question hours
• Communication with students
Am I addressing all the challenges?
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Calculus -
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Teaching Calculus – A Symbolic Approach
Problem I:Find Horizontal Asymptotes and
Absolute Maxima and Minima for below
function
23
385)(
2
2
x
xxxF
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Where is Symbolic Computing Used in Academia?
Mathematics departments
– Foundation courses: Calculus, Differential equations, Linear algebra, …
– Advanced courses: Number theory, Advanced algebra, …
Engineering departments
– Mechanical: System dynamics, Heat transfer, Fluid dynamics, …
– Electrical: Circuit analysis, Signals and systems, …
– Civil: Solid mechanics, Structural analysis, …
Physics and science departments
– Physics: Mechanics, Optics, Electromagnetics
– Life sciences: Bioinformatics, Computational Biology, Systems Biology
Anyone applying engineering, scientific, and mathematical principles to
find analytical solutions to technical problems
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Symbolic Math Toolbox Libraries
Calculus Differentiation
Integrals (definite, indefinite)
Jacobian
Taylor series
Limits
Solving EquationsAlgebraic Equations
Ordinary Differential Equations
Integral and Z-
Transforms Fourier transform
Laplace transform
Z-transforms
Simplification Expansion of polynomials
Substitution
Linear AlgebraOperations
Eigenvalues
Special Functions Bernoulli, Bessel, Beta, …
Fresnel sine/cosine integral,
Gamma
Variable Precision Arithmetic
Plotting2-D
3-D contour, surface, mesh
Movies
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Symbolic Math Toolbox Use in Curriculum
“Exploring Mathematics with MuPad” Course
• Course developed by
Dr. Catherine Wilkins
(University of Oxford)
• 56 page reference
document on getting
started with MuPAD
notebook interface
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Teaching Math – An Interactive Approach
Problem II:You have been launched as a human
cannonball from ground level at an initial
velocity (v_i) of 50 m/s at an angle
theta.
How high do you go before falling
back down?
How long where you in the air?
g
vYPosition
y
)2sin(_
2
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Experimenting with Live Signals
Acquire live signals from sensors, oscilloscopes and instruments
Perform analysis and experiments on live data
Live Acquisition of Your Own Voice Perform Discrete Fourier Transform
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MATLAB Connects to Your Hardware Devices
Data Acquisition Toolbox
Plug-in data acquisition devices
and sound cards
Instrument Control Toolbox
Instruments and RS-232
serial devices
MATLAB
Interfaces for communicating
with everything
Image Acquisition Toolbox
Image capture devices
Vehicle Network Toolbox
Vector & Kvaser CAN bus
interface devices
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Learning Beyond Classroom
Design interactive examples to explore what-if scenarios
Share examples as GUI executable with students
>> rlcdemo
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MATLABDesktop
End-UserMachine
1
2
3Toolboxes
Deploying Applications with MATLAB®
MATLAB®
Compiler™
.exe
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Give MATLAB code tostudents and other faculties who do not have MATLAB
• Use MATLAB Compiler to create stand-alone executable andshared libraries
• Use Compiler add-ons tocreate software components
.exe.dll
.lib
MATLAB® Compiler
Java
MATLABBuilder™ NE
MATLABBuilder™ EX
MATLABBuilder™ JA
Deploying Applications with MATLAB
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Agenda – Day 1Day 1
09:00 am - 09:30 am Registration
09:30 am - 09:45 am Welcome Address
09:45 am -11:00 am
MATLAB and Simulink in Engineering Education
MATLAB Basics for the Budding Engineer
•Experimentation and Modeling in MATLAB
•Design and Implementation
•Project-Based Learning (Arduino, RaspberryPi)
•Machine Learning & Data Analytics
11:00 am - 11:30 am Tea Break
11:30 am - 12:30 pm
•Accessing, exploring, analyzing, and visualizing data in MATLAB
•Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform
•Common machine learning tasks such as Feature selection and feature transformation
•Demo: ADAS using live camera stream
12:30 pm - 2:30 pm Lunch Break
02:30 pm - 04:30 pm
•Electrical engineering concepts Using MATLAB and Simulink
•System Identification & Neural Network Based System Modeling Techniques
•Electrical engineering using Simscape (Physical Modeling)
•Electrical engineering using SimPowersystems
•Control system design and analysis
•What is IoT?
• Market Drivers and Challenges
• Introduction to ThingSpeak
• Examples
• Other IoT examples using MW tools
• MATLAB and Simulink Capabilities for IoT
04:30 pm - 05:00 pm Tea Break
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Making Classroom Notes Living Documents
k
Wm
b
Wkxxbxm
x
Mass-Spring-Damper System
Example: Stability Analysis of
Second Order Systems
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Introduce blocks, (libraries), how to make a model
Hybrid systems - continuous and discrete components
Time-driven simulation of system model
Intuitive representation of a system
Design and Analysis with Simulink
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Teaching Multi-Domain Systems using Simscape™
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“It's not that I'm very smart; I am only passionately curious and
it's just that I stay with problems longer.”
-- Albert Einstein, Theoretical Physicist
• Interactive tools allows students to explore more and stay with problem longer.
Let Students Explore More
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SimulinkRun on target hardware
With a click, your model runs on
target hardware
Supported target hardware:
– R2013a: Raspberry Pi®
– R2013a: Gumstix® Overo®
– R2012b: PandaBoard
– R2012a: Arduino®, LEGO®
MINDSTORMS® NXT and
BeagleBoard
Run Simulink models on low-cost target
hardware
new
Raspberry Pi ®
new
PandaBoard
Arduino® LEGO® MINDSTORMS® NXT
BeagleBoard
Gumstix® Overo®
new
new
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Demo
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How it Works
WheelsMotor
Gyro Sensor
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Modeling the Segway Dynamics
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Different modeling approaches
DSP FPGAASIC
Embedded Software
Digital Electronics
C, C++VHDL,
Verilog
Integr
ation
MCU
Data
Modeli
ng
Algorit
hm
Devel
opme
nt
Implement V&V
Physical Modeling (Schematic)
Simulink (Block diagram)
DATA Driven
+-
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Modeling the Controller
Know your plant/system -Linearization
PID
Design the controller with linear plant model
Test the controller in real time
Test controller with non-linear plant in closed-loop
Design state-logic and supervisory logic
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Testing the Controller
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What our tools offer?
Simulink solution
– Real-Time Toolbox for LEGO Platform enables you to execute Simulink® and
Stateflow® models on a LEGO MINDSTORMS hardware platform for rapid control
prototyping
– hardware-in-the-loop (HIL) simulation, and other real-time applications
– library of I/O device drivers for LEGO MINDSTORMS sensors and actuators, a real-
time kernel, and an interface for real-time monitoring, parameter tuning, and data
logging.
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LEGO MINDSTORMS NXT Block Library
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Installing Target Library
Get from the MATLAB Toolstrip: Add-Ons
Get Hardware Support Packages
Get from the MATLAB Command Line: >>
targetinstaller
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© 2013 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names
may be trademarks or registered trademarks of their respective holders.
Additional Resources
www.mathworks.com/academia
http://www.mathworks.com/hardwa
re-support
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Machine Learning is Everywhere
Image Recognition
Speech Recognition
Stock Prediction
Medical Diagnosis
Data Analytics
Robotics
and more…
[TBD]
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Machine Learning
Machine learning uses data and produces a program to perform a task
Standard Approach Machine Learning Approach
𝑚𝑜𝑑𝑒𝑙 = <𝑴𝒂𝒄𝒉𝒊𝒏𝒆𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈𝑨𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎
>(𝑠𝑒𝑛𝑠𝑜𝑟_𝑑𝑎𝑡𝑎, 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦)
Computer
Program
Machine
Learning
𝑚𝑜𝑑𝑒𝑙: Inputs → OutputsHand Written Program Formula or Equation
If X_acc > 0.5
then “SITTING”
If Y_acc < 4 and Z_acc > 5
then “STANDING”
…
𝑌𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦= 𝛽1𝑋𝑎𝑐𝑐 + 𝛽2𝑌𝑎𝑐𝑐+ 𝛽3𝑍𝑎𝑐𝑐 +
…
Task: Human Activity Detection
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Example: Human Activity Learning Using Mobile Phone Data
Machine
Learning
Data:
3-axial Accelerometer data
3-axial Gyroscope data
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Challenges in Machine LearningHard to get started
Steps Challenge
Access, explore and analyze
dataData diversity
Numeric, Images, Signals, Text – not always tabular
Preprocess dataLack of domain tools
Filtering and feature extraction
Feature selection and transformation
Train modelsTime consuming
Train several models to find the “best”
Assess model performanceAvoid pitfalls
Over Fitting
Speed-Accuracy-Complexity tradeoffs
Iterate
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MODEL
PREDICTION
Machine Learning Workflow
Train: Iterate till you find the best model
Predict: Integrate trained models into applications
MODELSUPERVISED
LEARNING
CLASSIFICATION
REGRESSION
PREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
LOAD
DATAPREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
NEW
DATA
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Machine Learning
– What is Machine Learning and why do we need it?
– Common challenges in Machine Learning
Example 1: Human activity learning using mobile phone data
– Learning from sensor data
Example 2: Real-time car identification using images
– Learning from images
Summary & Key Takeaways
Agenda
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Example 1: Human Activity Learning Using Mobile Phone Data
Objective: Train a classifier to classify
human activity from sensor data
Data:
Approach:
– Extract features from raw sensor signals
– Train and compare classifiers
– Test results on new sensor data
Predictors 3-axial Accelerometer and
Gyroscope data
Response Activity:
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PREDICTIONMODEL
Machine Learning Workflow for Example 1
Train: Iterate till you find the best model
Predict: Integrate trained models into applications
MODELSUPERVISED
LEARNING
CLASSIFICATION
REGRESSION
PREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
LOAD
DATA
PREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
TEST
DATA
1. Mean
2. Standard
deviation
3. PCA
Classification
Learner
1. Mean
2. Standard
deviation
3. PCA
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Machine Learning
– What is Machine Learning and why do we need it?
– Common challenges in Machine Learning
Example 1: Human activity learning using mobile phone data
– Learning from sensor data
Example 2: Real-time car identification using images
– Learning from images
Summary & Key Takeaways
Agenda
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Example 2: Real-time Car Identification Using Images
Objective: Train a classifier to identify car
type from a webcam video
Data:
Approach:
– Extract features using Bag-of-words
– Train and compare classifiers
– Classify streaming video from a webcam
Predictors Several images of cars:
Response NIGEL, LIGHTNING, SANDDUNE, MATER
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PREDICTIONMODEL
Machine Learning Workflow for Example 2
Train: Iterate till you find the best model
Predict: Integrate trained models into applications
MODELSUPERVISED
LEARNING
CLASSIFICATION
REGRESSION
PREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
LOAD
DATA
PREPROCESS
DATA
SUMMARY
STATISTICS
PCAFILTERS
CLUSTER
ANALYSIS
WEBCAM
1. Build Bag-of-
features
2. Encode images
as new features
Classification
Learner
Encode images as
new features
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Machine Learning
– What is Machine Learning and why do we need it?
– Common challenges in Machine Learning
Example 1: Human activity learning using mobile phone data
– Learning from sensor data
Example 2: Real-time car identification using images
– Learning from images
Summary & Key Takeaways
Agenda
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MATLAB Strengths for Machine Learning
Steps Challenge Solution
Accessing, exploring and
analyzing dataData diversity
Extensive data supportImport and work with signal, images, financial,
Textual, geospatial, and several others formats
Preprocess data Lack of domain toolsHigh-quality libraries
Industry-standard algorithms for Finance, Statistics, Signal,
Image processing & more
Train models Time consuming Interactive, app-driven workflows
Focus on machine learning, not programing
Assess model
performance
Avoid pitfallsOver Fitting,
Speed-Accuracy-Complexity
Integrated best practicesModel validation tools built into app
Rich documentation with step by step guidance
IterateFlexible architecture for customized workflows
Complete machine learning platform
Challenges in Machine Learning
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Consider Machine Learning when:
– Hand written rules and equations are too complex
Face recognition, speech recognition, recognizing patterns
– Rules of a task are constantly changing
Fraud detection from transactions, anomaly in sensor data
– Nature of the data changes and the program needs to adapt
Automated trading, energy demand forecasting, predicting shopping trends
MATLAB for Machine Learning Email me if you have further questions
Key Takeaways
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Agenda – Day 1Day 1
09:00 am - 09:30 am Registration
09:30 am - 09:45 am Welcome Address
09:45 am -11:00 am
MATLAB and Simulink in Engineering Education
MATLAB Basics for the Budding Engineer
•Experimentation and Modeling in MATLAB
•Design and Implementation
•Project-Based Learning (Arduino, RaspberryPi)
•Machine Learning & Data Analytics
11:00 am - 11:30 am Tea Break
11:30 am - 12:30 pm
•Accessing, exploring, analyzing, and visualizing data in MATLAB
•Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform
•Common machine learning tasks such as Feature selection and feature transformation
•Demo: ADAS using live camera stream
12:30 pm - 2:30 pm Lunch Break
02:30 pm - 04:30 pm
•Electrical engineering concepts Using MATLAB and Simulink
•System Identification & Neural Network Based System Modeling Techniques
•Electrical engineering using Simscape (Physical Modeling)
•Electrical engineering using SimPowersystems
•Control system design and analysis
•What is IoT?
• Market Drivers and Challenges
• Introduction to ThingSpeak
• Examples
• Other IoT examples using MW tools
• MATLAB and Simulink Capabilities for IoT
04:30 pm - 05:00 pm Tea Break
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Agenda – Day 2
Day 2
10:00 am -11:00 pm
•Mechanical engineering and Control System Concepts Using MATLAB
and Simulink.
Mechanical engineering concepts using Simscape (Physical Modeling)
•Multi-Body Dynamics Simulation using SimMechanics
11:00 am - 11:30 am Tea Break
11:30 am - 01:00 pm
•Import CAD Models using GetMechanics App
•Implement Control on Low cost hardware - Arduino
•Demo of Magnetic Levitation System
1:00 pm - 2:00 pm Lunch Break
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What wakes you up in the morning?
Professors
StudentsResearchers
• Able to analyse all the data?
• Discovering at the right pace?
• Spending your time effectively?
• Accessing the right tools to
collaborate in a multi-disciplinary
environment
• Leveraging the computer power?
• Attract, engage, and retain students?
• Equip them to succeed in industry?
• Educate students with theory, modelling, hands-on
practice, and a systems perspective?
• Going to find a job?
• Able to bring innovation?
• Be competitive in a global economy?
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Student Algorithms in Action
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Student Competitions- IIT Bombay Racing team
• The only Indian car ever
to finish endurance at
Formula Student UK.
• EVo 3.0 was among
only 9 Electric Cars (out
of 32) to finish the
endurance run at FS
UK'14
• A professional outlook towards the project, healthy relationship with the
industry and a good testing time for the car, helped us rise to a respectable
performance at Formula Student this season
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BAJA SAE India
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Vibrational analysis of dynamic mechanical system using
Simulink and Simscape
How do we model 2 DOF mechanical system?
DOF: Degrees-Of-Freedom
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Vibrational analysis of dynamic mechanical system using
Simulink and Simscape
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Vibrational analysis of dynamic mechanical system using
Simulink and Simscape
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Vibrational analysis of dynamic mechanical system using
Simulink and Simscape
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Content
Importing CAD models
Automatically Create SimMechanics Models using GetMechanics App
Performing Co-Simulation with SimWise 4D
Simulating Controls, FEA, Dynamics, Thermal, Sound and Vibration in one
go.
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Importing CAD models
Open the CAD model in Simwise 4D
SimWise converts the CAD model into
SimMechanics convertible format (*.wm3)
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Automatically Create SimMechanics Models using
GetMechanics App
GetMechanics App can import
SimWise *.wm3 file
To get GetMechanics App
Contact MathWorks
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Performing Co-Simulation with SimWise 4D
Use MATLAB for Controls Modeling and Leverage MATLAB capabilities.
Perform Plant Simulation in SimWise 4D for Multibody Dynamics, FEA, Heat, Vibration, Buckling,
Durablity and Realiability.
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Simulating Controls, FEA, Dynamics, Thermal, Sound and
Vibration in one go.
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Performing multi-body dynamic system using SimMechanics
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