RMU_Summer2005_Samanta1 Applications of Computational Intelligence Techniques in Engineering B Samanta International Visiting Professor Robert Morris University

RMU_Summer2005_Samanta 1

Applications of Computational Intelligence Techniques in Engineering

B SamantaInternational Visiting Professor

Robert Morris University


Presentation Summary Motivation Computational Intelligence Different CI techniques Applications of CI techniques Recent Work Work done at RMU Way forward Conclusions


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.


Computational Intelligence (CI)

Intelligence built in computer programs

Covers Evolutionary computing Fuzzy computing Neuro-computing

Also known as Soft computing


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)


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)


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


ANN- a typical example

x1

x2

xN

u1

u2

uQ

y1

y2

yM

.

. ..

.

.

Input layer Hidden layer


Fuzzy Logic

Steps involved Fuzzification using membership

functions (MFs)-input Generation of rule base Aggregation Defuzzification using MFs -output


Fuzzy Logic (contd.)

Input and output MFs Number Type Parameters

Rule base (experience guided)


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


Fuzzy Logic – An Example

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


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


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


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.


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


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).


GP output –An Example Terminals (leaves): inputs x1, x2 and constant 3 Nodes: Math functions *,+, exp Output: x1*x2+exp(3)

X1X2

times

plus

exp

3

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


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


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


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


Recent work (contd.)

Materials ANN based estimation of fatigue life Modeling of material properties in

terms of heat treatment parameters Rotordynamics Control System Design


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)


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


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


0 5 10 15 20 25-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

index i

Nor

mal

ized

Too

l Life

Fig. 1. Results of training data set

Actual

PredictedPrediction error


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

index i

Nor

mal

ized

Too

l Life

Fig. 2. Results of test data set

Actual



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


Acrobat Document


0 5 10 15 20 25 30 35 40-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

index i

Nor

mal

ized

too

l fla

nk w

ear

Fig. 1. Results of training data set

Actual



0 5 10 15 20 25-0.2

0

0.2

0.4

0.6

0.8

1

1.2

index i

Nor

mal

ized

too

l fla

nk w

ear

Fig. 2. Results of test data set

Actual



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


X2

X2

X3

X2

X1

X2

step

asin

plus

power

X4

X3

step

asin

plus

powerX4

divide

log10

tanh avg

X2

acos

log

X3

X2

X4

X3

step

asin

plus

powerX3

divide

log10

tanh

avg

power

asin power

exp

sqrt

power

exp

sqrt

power

GP output tree for Roughness model


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


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


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


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


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


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


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

Documents

RMU_Summer2005_Samanta1 Applications of Computational Intelligence Techniques in Engineering B Samanta International Visiting Professor Robert Morris University