Applications of Computational Intelligence Techniques in Engineering

boorishadamantAI and Robotics

Oct 29, 2013 (3 years and 9 months ago)

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Applications of Computational
Intelligence Techniques in
Engineering

B Samanta

International Visiting Professor

Robert Morris University


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Presentation Summary


Motivation


Computational Intelligence


Different CI techniques


Applications of CI techniques


Recent Work


Work done at RMU


Way forward


Conclusions


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Motivation


Use of computers for better understanding and
interpretation of process/system behavior


Use of available information to obtain input
-
output
mapping.


Utilization of expert/operator knowledge


Ability to use imprecise, uncertain information


Integration of knowledge over multiple disciplines


Automated machine learning inspired from nature
(neuroscience, genetics, behavioral science)


Development of models for optimizing the system
performance satisfying the inherent system/process
constraints.

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Computational Intelligence
(CI)


Intelligence built in computer programs


Covers


Evolutionary computing


Fuzzy computing


Neuro
-
computing


Also known as


Soft computing



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CI Techniques


Artificial Intelligence (AI)


Artificial Neural Networks (ANNs)


Fuzzy Logic (FL)


Support Vector Machines (SVM)


Self Organizing Maps (SOM)
-

unsupervised


Genetic Algorithm (GA)


Genetic Programming (GP)


Swarm Intelligence/Particle Swarm
Optimization (PSO)


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CI Techniques (contd.)


ANNs


Multi
-
layer Perceptron (MLP)


Radial Basis Function (RBF)


Probabilistic Neural Network (PNN)


Fuzzy Logic + ANN


Adaptive neuro
-
fuzzy inference system
(ANFIS)

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CI Techniques (contd.)

ANN structure


Input layer


Hidden Layer (s)


Output layer


Number of nodes in each layer


Functions and their parameters


Mostly decided on trial and error basis

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ANN
-

a typical example

x
1

x
2

x
N

u
1

u
2

u
Q

y
1

y
2

y
M

.

.

.

.

.

.

Input layer

Hidden layer

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Fuzzy Logic

Steps involved


Fuzzification using membership
functions (MFs)
-
input


Generation of rule base


Aggregation


Defuzzification using MFs
-
output


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Fuzzy Logic (contd.)


Input and output MFs


Number


Type


Parameters


Rule base (experience guided)



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Neuro
-
Fuzzy System


Combines the advantages of fuzzy logic
(FL) and ANNs


Starts with an initial FL structure


Uses ANN for adapting the FL (MF)
parameters and the rule base to the
training data


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Fuzzy Logic


An Example

ANFIS structure for an example system with 2 inputs and 1 output.

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Snapshot of rule base for an example system with 2 inputs and 1 output.

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Genetic Algorithms


Construction of genome (individual)


Generation of initial population (group of individuals)


Evaluation of individuals


Selection of individuals based on criteria


Generation of new individuals


Mutation


Crossover


Repetition of the process
-

generation, evaluation,
selection


Termination of the process based on max generation
no. and/or performance criteria


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Combinations


Combine advantages of GA and other classifiers


GA and ANN


GA and ANFIS


GA and SVM


for automatic selection of classifier structure and parameters


ANNs
-
Number of neurons in hidden layer


ANFIS
-

Number of MFs and their parameters


SVM


SVM parameters


Selection of most important system features from a pool


Selection of most important sensors (in the context of on
-
line
condition monitoring and diagnostics)
-

sensor fusion.





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Signal Conditioning and Data Acquisition

Feature Extraction

Training Data Set

Test Data Set

Training of ANN/ SVM

Is ANN/ SVM
Training
Complete ?

No

Yes

ANN / SVM Output

Machine Condition Diagnosis

Trained ANN/ SVM with selected features

Fig. 1. Flow chart of diagnostic procedure


GA based selection of features and
parameters

Is GA based
selection
over?

Yes

No

Rotating Machine with Sensors

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Genetic Programming (GP)


GP


a branch of GA with a lot of similarities.


Main difference of GP and GA is in the
representation of the solution.


In GA, the output is in form of a string of
numbers representing the solution.


GP produces a computer program in form of
a tree
-
based structure relating


the inputs (leaves)


the mathematical functions (nodes) and


the output (root node).


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GP output

An Example


Terminals (leaves): inputs x1, x2 and constant 3


Nodes: Math functions *,+, exp


Output: x1*x2+exp(3)

X1

X2

times

plus

exp

3

(+ (* (X1 X2))(exp(3))

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Applications


Computer Science


Pattern Recognition (PR)


Data Mining


Knowledge Discovery/ Machine Learning


Feature Extraction and Selection


Mechanical Systems


Condition monitoring and diagnostics


Multiobjective optimization in design


Control System Design


Manufacturing Systems


Development of data
-
driven models


Multiobjective optimization of machining parameters


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Applications (contd.)


Engineering Management/IE


Inventory management


Project selection


Facility layout design


Scheduling


Medicine


Patient condition monitoring and diagnosis


Social Science


Business


Market analysis and forecasting


Credit rating

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Recent Work


Machine Condition Monitoring and Diagnostics
using


ANNs
-
MLP, RBF, PNN



SVM


ANFIS


GA
-
ANN


GA
-
ANFIS


GA
-
SVM


GP


Involving signal processing, feature
extraction, selection and sensor fusion


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Recent work (contd.)


Materials


ANN based estimation of fatigue life


Modeling of material properties in terms of
heat treatment parameters


Rotordynamics


Control System Design




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Work done at RMU


Intelligent Manufacturing Systems


Development of Tool Wear Model


ANFIS and GA
-
ANFIS


Genetic Programming (GP)


Development of machined surface roughness model


ANFIS and GA


Genetic Programming (GP)


Mutliobjective optimization of machining parameters


Minimization of machining cost


Minimization of surface roughness


Minimization of production time


Subject to constraints on


Operating parameters

speed, feed, depth of cut


Cutting Force


Power consumption


Tested on 5 different data sets


Involves different machining operations


Milling,



turning and


Turning of hard material (>Rc 65)



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Tool Wear Model


Mapping of Inputs and Outputs


Inputs


Tool type
-

geometry, material


Work piece


Cutting speed (V)


Feed rate (f)


Depth of cut (d)


Vibration (Vx, Vy, Vz)


Forces (Fx, Fy, Fz)


Cutting Time (t)


Outputs


Tool wear


Remaining Tool Life


GA/GP based selection of characteristic inputs




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ANFIS based Tool Wear Model


An Example


Input pool


Spindle speed (x1)


Feed rate (x2)


Machining time (x3)


Ratio of forces in 2 directions: Fx (feed)/ Fz (tangential) (x4)



Output


Tool wear level


Data set


Training


25


Test
-

38


Number of MFs
-

2


Performance



Training Root Mean Square Error (RMSE) 1.30%


Test data set RMSE : 8.52%


Training time 0.34 s



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GA
-
ANFIS based roughness
model


An Example


Input pool


Spindle speed (x1)


Feed rate (x2)


Depth of cut (x3)


Vibration in 3 directions


x (radial) (x4)



y (tangential) (x5)



z (feed) (x6)


Output


surface roughness


Data set


Training


36


Test
-

24


GA based selection of best 3 features: x2, x1, x5


Number of optimum MFs
-

2


Performance



Training Root Mean Square Error (RMSE) 2.60%


Test data set RMSE : 6.65%


Training time 263.2 s



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GP model for surface
roughness


GP was used for same data sets


Training


36


Test set


24


Performance


Training RMSE: 3.79%


Test RMSE : 6.90%


Training time: 463.7 s

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GP output tree for Roughness model

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Publications Planned


Predictive modeling of tool wear in turning
using adaptive neuro
-
fuzzy inference system


Modeling and prediction of tool wear in
turning using genetic programming


Predictive modeling of surface roughness in
turning using adaptive neuro
-
fuzzy inference
system and genetic algorithms

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Publications Planned (contd.)


Modeling and prediction of surface roughness
in turning using genetic programming


Predictive modeling of surface roughness in
milling using adaptive neuro
-
fuzzy inference
system and genetic algorithms


Multiobjective evolutionary optimization of a
machining process

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Conferences/Journals


North American Manufacturing Research Conference
(NAMRC 34 ), NAMRI/SME, May 23
-
26, 2006,
Milawukee, WI, USA.


Flexible Automation and Intelligent Manufacturing
(FAIM) June 26
-
28, 2006, Univ of Limerick, Ireland.


IFAC Symposium on Information Control in
Manufacturing (INCOM) May17
-
19, 2006, France.


Journal of Manufacturing Systems/SME


International Journal of Machine Tools & Manufacture

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Industry
-
RMU collaboration
Potential


Interest in RMU
-
EOC research collaboration in
the area of Laser machining.


Development of machining models using CI


Multiobjective constrained optimization of
machining/laser system parameters


Sensor fusion



Interest in RMU
-
ExOne research collaboration
in the areas of 3D printing


process


system


Design optimization



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Way Forward


Scope for further collaboration with
RMU


Teaching


Development of new elective or
short courses in consultation with Faculty


Research


Joint supervision of projects/theses
at Senior, MS and PhD levels


Collaborative work with Faculty


Outreach
-

Industry and Government supported
research projects/contracts

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Conclusions

Increasing popularity of CI techniques


Integrating capability over multiple disciplines


Capability of incorporating imprecision and
uncertainty


Suitability for hard
-
to
-
model processes
/systems


Better alternatives to traditional hard
computing scenario


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THANKS

Thanks to



RMU Administration



Sponsor of the Program



SEMS/Engineering Faculty, Staff

for the support and facilitating the visit

Thanks to you all (in audience)


For your time and patience