Download Applications of Computational Intelligence Techniques in

Applications of Computational
Intelligence Techniques in
Engineering
B Samanta
International Visiting Professor
Robert Morris University
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Presentation Summary
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Motivation
Computational Intelligence
Different CI techniques
Applications of CI techniques
Recent Work
Work done at RMU
Way forward
Conclusions
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Motivation
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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)
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Intelligence built in computer programs
Covers
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Evolutionary computing
Fuzzy computing
Neuro-computing
Also known as
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Soft computing
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CI Techniques
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Artificial Intelligence (AI)
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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.)
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ANNs
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Multi-layer Perceptron (MLP)
Radial Basis Function (RBF)
Probabilistic Neural Network (PNN)
Fuzzy Logic + ANN
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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
x1
x2
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xN
Input layer
u1
Hidden layer
u2
.
u.
Q
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y1
y2
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yM
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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.)
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Input and output MFs
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Number
Type
Parameters
Rule base (experience guided)
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Neuro-Fuzzy System
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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
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Construction of genome (individual)
Generation of initial population (group of individuals)
Evaluation of individuals
Selection of individuals based on criteria
Generation of new individuals
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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
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Combine advantages of GA and other classifiers
GA and ANN
GA and ANFIS
GA and SVM
for automatic selection of classifier structure and parameters
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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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Rotating Machine with Sensors
Signal Conditioning and Data Acquisition
Feature Extraction
Training Data Set
Test Data Set
GA based selection of features and
parameters
Training of ANN/ SVM
No
No
Is ANN/ SVM
Training
Complete ?
Yes
Is GA based
selection
over?
Yes
Trained ANN/ SVM with selected features
ANN / SVM Output
Machine Condition Diagnosis
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Fig. 1. Flow chart of diagnostic procedure
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Genetic Programming (GP)
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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
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the inputs (leaves)
the mathematical functions (nodes) and
the output (root node).
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GP output –An Example
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Terminals (leaves): inputs x1, x2 and constant 3
Nodes: Math functions *,+, exp
Output: x1*x2+exp(3)
(+ (* (X1 X2))(exp(3))
plus
exp
times
X1
X2
3
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Applications
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Computer Science
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Mechanical Systems
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Pattern Recognition (PR)
Data Mining
Knowledge Discovery/ Machine Learning
Feature Extraction and Selection
Condition monitoring and diagnostics
Multiobjective optimization in design
Control System Design
Manufacturing Systems
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Development of data-driven models
Multiobjective optimization of machining parameters
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Applications (contd.)
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Engineering Management/IE
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Medicine
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Inventory management
Project selection
Facility layout design
Scheduling
Patient condition monitoring and diagnosis
Social Science
Business
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Market analysis and forecasting
Credit rating
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Recent Work
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Machine Condition Monitoring and Diagnostics
using
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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.)
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Materials
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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
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Intelligent Manufacturing Systems
Development of Tool Wear Model
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Development of machined surface roughness model
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ANFIS and GA-ANFIS
Genetic Programming (GP)
ANFIS and GA
Genetic Programming (GP)
Mutliobjective optimization of machining parameters
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Minimization of machining cost
Minimization of surface roughness
Minimization of production time
Subject to constraints on
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Operating parameters –speed, feed, depth of cut
Cutting Force
Power consumption
Tested on 5 different data sets
Involves different machining operations
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Milling,
turning and
Turning of hard material (>Rc 65)
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Tool Wear Model
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Mapping of Inputs and Outputs
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Inputs
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Outputs
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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)
Tool wear
Remaining Tool Life
GA/GP based selection of characteristic inputs
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ANFIS based Tool Wear Model
– An Example
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Input pool
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Output – Tool wear level
Data set
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Spindle speed (x1)
Feed rate (x2)
Machining time (x3)
Ratio of forces in 2 directions: Fx (feed)/ Fz (tangential) (x4)
Training – 25
Test - 38
Number of MFs - 2
Performance –
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Training Root Mean Square Error (RMSE) 1.30%
Test data set RMSE : 8.52%
Training time 0.34 s
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Fig. 1. Results of training data set
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Actual
Predicted
Prediction error
0.8
0.7
Normalized Tool Life
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0.5
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0
-0.1
0
5
10
15
20
25
index i
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Fig. 2. Results of test data set
1.2
Actual
Predicted
Prediction error
1
Normalized Tool Life
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0.6
0.4
0.2
0
-0.2
-0.4
0
5
10
15
20
index i
25
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GA-ANFIS based roughness
model – An Example
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Input pool
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Spindle speed (x1)
Feed rate (x2)
Depth of cut (x3)
Vibration in 3 directions
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Output – surface roughness
Data set
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x (radial) (x4)
y (tangential) (x5)
z (feed) (x6)
Training – 36
Test - 24
GA based selection of best 3 features: x2, x1, x5
Number of optimum MFs - 2
Performance –
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Training Root Mean Square Error (RMSE) 2.60%
Test data set RMSE : 6.65%
Training time 263.2 s
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Fig. 1. Results of training data set
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Actual
Predicted
Prediction error
0.8
0.7
Normalized tool flank wear
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0.5
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0
-0.1
0
5
10
15
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index i
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Fig. 2. Results of test data set
1.2
Actual
Predicted
Prediction error
1
Normalized tool flank wear
0.8
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0.4
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0
-0.2
0
5
10
15
20
25
index i
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GP model for surface
roughness
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GP was used for same data sets
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Training – 36
Test set – 24
Performance
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Training RMSE: 3.79%
Test RMSE : 6.90%
Training time: 463.7 s
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GP output tree for Roughness model
power
sqrt
X2
exp
power
sqrt
X2
exp
power
avg
tanh
X3
power
power
log10
log
divide
acos
power
X4
avg
tanh
X3
log10
X2
plus
plus
X2
asin
asin
X4
asin
X1
step
divide
step
X3
power
X3
plus
X2
asin
X4
step
X2
X3
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Publications Planned
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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.)
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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
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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
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Interest in RMU-EOC research collaboration in
the area of Laser machining.
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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
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process
system
Design optimization
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Way Forward
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Scope for further collaboration with
RMU
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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
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