PROJECT PHASE-II REVIEW

“PREDICTION OF METAL REMOVAL RATE AND SURFACE ROUGHNESS OF AN ELECTRO

CHEMICAL MACHINING PROCESS USINGARTIFICIAL NEURAL NETWORK ”

By V.Suresh Guided by – Prof.S.AYYAPPAN.,M.E.,04/10/23 1

Electro Chemical Machining (ECM) is capable of machining geometrically complex material components, composites, super alloys, ceramics, carbides and heat resistant steels which are widely used in aerospace, nuclear and die making Industries.

Fig 1 Principle of Working

OBJECTIVES :

Maximize the Metal Removal Rate(MRR)

Minimize the Surface roughness(Ra)

Process parameters Electrolyte flow rate (U)= 6-10 lit/min Feed rate (F)= 0.1-0.54 mm/min Voltage (V)= 15-25 volts

Fig 2 ECM equipment 

Fig 2 ECM equipment

Mathematical model for MRR and SR using MINITAB software

MRR = -0.0765+0.0511U+0.7181F+0.006V-0.001U^2 -0.6812F^2+0.0001V^2+0.0124UF-0.0001UV-0.0074FV

SR = 4.473-0.0676U-0.4963F+0.0163V-0.014U^2 +0.9412F^2+0.0001V^2+0.0663UF+0.0003UV-0.0045FV

Where, MRR = Metal removal rate SR = Surface roughness U = Electrolyte flow rate F = Feed rate V = Voltage

0.125

0.150

0.175 0.200

0.50.40.30.20.1

25

24

23

22

21

20

19

18

17

16

15

F

V

Contour Plot of MRR

Hold values: U: 0.0

F Vs V

U Vs V

0.35 0.40

0.45

0.50

6 7 8 9 10

15

16

17

18

19

20

21

22

23

24

25

U

VContour Plot of MRR

Hold values: F: 0.0

U Vs F

0.38

0.48 0.58

109876

0.5

0.4

0.3

0.2

0.1

U

F

Contour Plot of MRR

Hold values: V: 0.0

F Vs V

5.00

5.05 5.10

5.15

0.1 0.2 0.3 0.4 0.5

15

16

17

18

19

20

21

22

23

24

25

F

V

Contour Plot of R

Hold values: U: 0.0

U vs v

3.25

3.50 3.75

4.00

4.25

6 7 8 9 10

15

16

17

18

19

20

21

22

23

24

25

U

V

Contour Plot of R

Hold values: F: 0.0

U vs F

3.05

3.30 3.55

3.80

4.05

6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

U

F

Contour Plot of R

Hold values: V: 0.0

VFU

4.20

3.95

3.70

3.45

3.20

RMain Effects Plot - Data Means for R

U F V

0.44

0.48

0.52

0.56

0.60

MR

R

Main Effects Plot - Data Means for MRR

OptimalD

1.0000 Lo

HiCur

MRRMaximum

d = 1.0000

RMinimum

d = 1.0000

y = 0.6214

y = 3.2987

10.0

6.0

0.540

0.10

25.0

15.0

F VU

[10.0] [0.540] [15.0]

Models of the brain and nervous system Highly parallel

◦ Process information much more like the brain than a serial computer

Learning Very simple principles Very complex behaviours

ANNs incorporate the two fundamental components of biological neural nets:

1. Neurons (nodes)

2. Synapses (weights)

Information flow is unidirectional

Data is presented to Input layer

Passed on to Hidden Layer

Passed on to Output layer

Information is distributed

Information processing is parallel

Internal representation (interpretation) of data

Back propagation

• Requires training set (input / output pairs)• Starts with small random weights• Error is used to adjust weights (supervised learning)• Gradient descent on error landscape

1. Initialize the weights in the network (often randomly) 2. repeat for each e in the training set do

a. O = neural-net-output(network, e) ; forward pass b. T = teacher output for e c. Calculate error (T - O) at the output units d. Compute error term Di for the output node

e. Compute error term Dih for nodes of the intermediate layer

f. Update the weights in the network Dwij=a*ai*Dj

until all inputs classified correctly or stopping criterion satisfied

3. return(network)

MRR

SR

F

U

V

Experimental Data’s:

U F V MRR R

lit/min mm/min Volt 10‾³kg/min μm

6 0.1000 15 0.3612 4.1100

6 0.1000 20 0.4019 4.1800

6 0.1000 25 0.4458 4.2800

6 0.3200 15 0.4312 4.2100

6 0.3200 20 0.4501 4.3000

6 0.3200 25 0.4669 4.4100

6 0.5400 15 0.4654 4.2900

6 0.5400 20 0.4717 4.3800

6 0.5400 25 0.5112 4.5100

8 0.1000 15 0.3987 3.6100

8 0.1000 20 0.4423 3.7200

8 0.1000 25 0.4796 3.8300

8 0.3200 15 0.5425 3.7300

U F V MRR R

lit/min mm/min Volt 10‾³kg/min μm

8 0.3200 20 0.5612 3.8200

8 0.3200 25 0.5912 3.9300

8 0.5400 15 0.5411 3.8100

8 0.5400 20 0.5701 3.9200

8 0.5400 25 0.5879 4.0200

10 0.1000 15 0.4987 3.0100

10 0.1000 20 0.541 3.1300

10 0.1000 25 0.5602 3.2400

10 0.3200 15 0.5909 2.9900

10 0.3200 20 0.6012 3.1400

10 0.3200 25 0.6523 3.2500

10 0.5400 15 0.6135 3.3800

10 0.5400 20 0.6411 3.4500

10 0.5400 25 0.6501 3.5100

Mean Square Error Vs Epochs

Epoch: 2000 Iteration: 27 Calculated Output: Hidden Layer (Z): 1.000000 1.000000 0.000000 1.000000 Output (Y): 0.739 0.000 Target (T): 1.001 -0.316 Output Error Information Term: 0.050515 -0.000022 Back Propagation Hidden Layer: -261.808672 -1153.493559 4962.172565 354.643815 -1231.684756 -2112.860792 -258.539886 -967.286913

Updated Vector:

43.499365 46.569241 -23.501805 41.212759

21.545410 23.080787 -47.508943 20.362101

11.666677 12.430252 -12.737709 11.186230 Updated Weight:

0.062012 -2.629617

0.985727 -4.395553

-5.214606 -0.667345

0.066378 -2.556346 Updated Weight Bias: 0.035361 -0.000015

Updated Vector Bias: 0.000000 0.000000 0.000000 0.00000

Experiment is designed and conducted based on Response Surface Methodology. Neural Network model is found for Electro chemical machining process.

Neural Network coding is done with C language and its performance is compared against the NN training with MATLAB.NN model is proved to be very efficient and accurate one than regression model.

The present work can be extended by integrating NN model with optimization techniques such as GA, PSO, SA, ACO etc.,.

[1] Dubey AK, Shan HS, Jain NK(2006)Analysis of surface roughness and out-of-roundness in electro-chemical honing process. Proc ist Int and 22nd AIMTDR,Roorkee(India)Dec21-23.Int J Adv Manuf Technol DOI10.1007/s00170-007-1180-z.

[2] Dubey AK, Shan HS, Jain NK(2006) precision micr-finishing by electro-chemical honing.Proc int confon Manuf Sci and Technol,Meaka,Aug28-30:173-176.Int J Manuf Technol &Management(in process.

[3] Acharya B.G, Jain V.K and Batra J.L (1986) ‘Multi-objective optimization of ECM process’, Precision Engineering (Butterworth & Co Ltd).

[4] Dubey A.K (2008) ‘A hybrid approach for multi-performance optimization of the electro-chemical honing process’, International Journal of Advanced Manufacturing Technology.

[5] Satishkumar ‘Neural Networks A Classroom Approach’, Tata McGraw-Hill Publishing Company limited, New Delhi.

THANK YOU