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7/31/2019 Dinesh National Conf Paper
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Development of ANN modeling for coefficient of friction of
electro deposited nickel-graphite composites
S.Dinesh1, G.N.K.RameshBapu
2, K.Ramanathan
3, T.LouieFrango
4
1P.G. scholar, A.C.College of Engineering &Tech, Karaikudi-4,[email protected],9003645530.
2Scientist, Central Electrochemical Research Institute, Karaikudi, 630006,
[email protected], 9486563834.3Assistant Professor of Mechanical Engg, A.C.College of Engineering&Tech,
Karaikudi-4.,[email protected],9994607024.4Assistant Professor of automobile Engg, Shanmuganathan Engineering College,
Pudukkottai 622507,[email protected] ,9443612523
Abstract:
Nickel-Graphite composite coatings are produced by electro deposition
using conventional techniques at various cathode current densities, pH and temperature.
Electro deposition was carried out from a conventional Watts bath. Natural graphite
powder of 20-30 m size was used in this study . 33
full factorial designs of experiments
were designed by adopting the Design of Experiments (DOE) approach with three level of
experiment namely Low, Medium and High. The volume percentage of graphite
deposition in composite coated specimens were measured gravimetrically. The coefficient
of friction of coated specimen was measured using scratch tester. An Artificial Neural
Network (ANN) model was developed using 27 practical data obtained to predict the co
efficient of friction of Ni-Graphite metal matrix. Within the range of input variables for
the present case (pH) = 3 to 5; current density (i) = 3 to 5 A/dm2; temperature (T)= 40 to
600C, the prediction capability of Artificial Neural Network(ANN) is very close to the
experimental measurement of friction of Ni-Graphite metal matrix.
Keywords: Coefficient of Friction, Scratch tester, ANN model, Ni-Graphite composite
coatings, MATLAB
1. INTRODUCTION
Particle-reinforced metal matrix composites generally exhibit wide engineering
applications due to their enhanced hardness, frictional resistance, wear and corrosion resistance
compared to pure metal or alloy. Composite electroplating has been identified to be a
technologically feasible and economically superior technique for the preparation of such kind of
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]7/31/2019 Dinesh National Conf Paper
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composites. Graphite is a natural material, is known to be the good lubricant material. Because
of their outstanding properties, Nickel-Graphite has been used in EMI gasket and shield
applications.
Because of its excellent mechanical and electrical properties Nickel-Graphite is
of great interest for a number of applications (e.g. bearings, engine parts, electronic gaskets,
etc.). Searching the literature, the impression arises that the applications of Graphite are kept a
bit secret. Most of the relevant references are patents (up to 90% depending on the topic) giving
less exact data about the process. Papers published in journals giving detailed information are
rare.
The volume percent incorporation of Graphite powder in the Ni-Graphite
composite coating measured gravimetrically was earlier demonstrated. The amount of Graphite
deposited in the composite metal matrix is mainly affected by the process parameters such as
current density, pH value, temperature of the path solution and concentration of Graphite
dispersed in the electrolyte. Volume fraction of Graphite influences the Coefficient of Friction
of Ni-Graphite composite coatings and hence it is essential to develop a prediction model for
estimating the Coefficient of Friction of Nickel-Graphite composite using the above parameters.
TABLE 1 Graphite incorporation in nickel
2.0EXPERIMENTAL PROCEDURE
2.1 The electrolyte:
The conventional Watts bath of the following composition was used:
Nickel sulphate - 225 g/l; Nickel chloride- 30 g/l; Boric acid- 40 g/l. The electrolyte was purified
S. NoGraphite
g/lit
Weight of
Graphite
(gm)
Weight of
nickel
(gm)
Vol. of
Graphite
(v1)
Vol. of
Nickel
(V2)
Vol. %
Of
graphite
Vol. %
Of nickel
1 5 0.0157 0.2416 0.0074 0.0271 20.62 79.38
2 10 0.0316 0.2348 0.0142 0.0263 35.07 64.93
3 20 0.061 0.2407 0.0273 0.02704 49.76 50.234 30 0.0094 0.2348 0.0042 0.0263 13.79 86.20
5 40 0.0086 0.2466 0.0038 0.0277 12.20 87.79
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in the conventional manner for removal of organic and inorganic impurities [5]. The pH value
of the electrolyte was adjusted electrometrically using dilute H2SO4 or NH3. 0.01-g/l sodium
lauryl sulphate was added to the electrolyte as anti-pitting agent before plating. The temperature
of the electrolyte was maintained using a thermostat.
2.2. Plating procedure:
Deposition was carried out on a 500 ml capacity using conventional technique.
Nickel anodes and mild steel cathodes were used. The cathodes of 7.52.5 cm area were
mechanically polished, degreased, bent to 90, suitably masked to expose an effective plating
area of 12.5 cm2, electro cleaned, first cathodically and then anodically, washed rinsed and then
introduced into the plating electrolyte with the area to be plated in the vertical plane closer to the
bottom of the cell facing the anode. A bagged nickel anode bent similarly was placed above the
area to be coated. Graphite powder (20 to 20 m) was added to the electrolyte in the form of
slurry. The solution was stirred using a magnetic stirrer. Stirring was given initially for 30 s to
bring all the Graphite powder into the suspension and then stopped. The deposition was
continued for 40 minutes to allow the particles to settle on the substrate while the deposition
proceeded. The same process was repeated to obtain various thicknesses.
Figure 1 Electro deposition experimental setup
2.3. Nickel-Graphite deposition:
Natural grade graphite powder of 2030 m sizes was used. Prior to the co-
deposition, the graphite particles were ultrasonically dispersed in the bath for 10 min.
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Experiments were conducted at a fixed Graphite concentration of 20 g/l, varying the plating
parameters like temperature, pH, and current density. Ranges of coating parameters in the
coating process are as follows:
Current density, I = 35 A/dm2 ; pH value = 35; Temperature = 40 to 600C .For the prediction of Coefficient of Friction of Graphite under a variation of coating
conditions, a training database with regard to different coating parameters needs to be
established. For the above combination of parameters, twenty seven numbers of Nickel-Graphite
composite coatings were obtained and their Coefficient of Friction was measured from scratch
tester.
2.4 Design of experiment:
Process parameter Units
Levels
Level 1 Level 2 Level 3
pH 3 4 5
Current density A/dm2
3 4 5
Temperature 0C 40 50 60
TABLE 2 Process parameters with different levels
S.NO CURRENT DENSITY (A/dm2)
TEMPERATURE (O
C) pH VALUE
1 2 50 4
2 4 50 4
3 6 50 4
4 2 60 4
5 4 60 4
6 6 60 4
7 2 40 4
8 4 40 4
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9 6 40 4
10 2 50 3
11 4 50 3
12 6 50 3
13 2 60 3
14 4 60 3
15 6 60 3
16 2 40 3
17 4 40 3
18 6 40 3
19 2 50 5
20 4 50 5
21 6 50 5
22 2 60 5
23 4 60 5
24 6 60 5
25 2 40 5
26 4 40 5
27 6 40 5
TABLE 3 27 Parameters-Nickel Graphite coatings
2.5 Coefficient of Friction:
Nickel- graphite composites have been tested through the Scratch tester with
constant loadcondition at starting load should be 10 Newtons, loading rate should be zero,
stroke length is 10mm, scratch speed should be 0.20 mm/sec and scratch offset is 0.25mm .Then
we have transfer loading condition enter the file name. Finally we have seen the view file and
how much of Coefficient of friction is obtained.
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Figure 2 Coefficient of friction experimental setup
2.5.1 Initial Loading Conditions
Figure 3 initial loading conditions for Coefficient of Friction
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2.5.2 Graphs shown Coefficient of Friction:
Figure 4 Coefficient of Friction
2.5.3 Scratch Depth:
Figure 5 scratch is depth measured
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3. ARTIFICIAL NEURAL NETWORK (ANN)
ANN is a neural system of imitative biology, and the principle of human brain
operation. Using a large amount of data out of which they build knowledge bases, ANN
establishes analytical model to solve the problem in the estimation, prediction, decision making
and diagnosis. Neural network consist of simple processors, which are called neurons, linked by
weighted connections. Each neuron has inputs and generates an output that can be seen as the
reflection of local information that is stored in connections. The output signal of a neuron is fed
to other neurons as input signals via interconnections. Since the capability of a single neuron is
limited, complex functions can be realized by connecting many neurons. It is widely reported
that structure of neural network, representation of data, normalization of inputs outputs and
appropriate selection of activation functions have strong influence on the effectiveness and
performance of the trained neural network . A Neural network consists of at least three layers
i.e., input layer, hidden layer, and output layer, where inputs are applied at the input layer and
outputs are obtained at the output layer and learning is achieved when the associations between a
specified set of input output pairs as established in Figure.1 .Here Feed forward back
propagation (FFBP) algorithm is used for the prediction of Coefficient of Friction of Nickel-
Graphite in the deposit for the given condition. Figure.vi shows the architecture of a standard
supervised training FFBP ANN and Figure vii shows the perception.
Figure 6 ANN Model with two hidden layers Figure7 a typical processing element (Perception).
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Figure. 8 Standard supervised training feed forward neural network
4.0 TRAINING THE ARTIFICIAL NEURAL NETWORK
The neural network has to be first trained and then tested to use for application.
The training was done with MATLAB software using a computer. MATLAB is a software
package used for high performance numerical computations and visualization. It provides an
interactive environment with hundreds of built in functions for technical computations, graphic
and animations. MATLAB stands for matrix lab. Built in functions provides excellent tools for
linear algebra computation data analysis, signal processing, optimization and others scientific
computations. In this work ANN module is utilized for predicting the Coefficient of Friction of
Graphite deposition in Nickel-Graphite composite matrix. The features current density, pH and
temperature are the inputs and the Coefficient of Friction of Nickel-Graphite is the output for
training the neural networks. Weights between input layer & hidden layer and weights between
hidden layer & the output layer are generated randomly for the selected topology of the network.
The number of patterns used for the training of Artificial Neural Network using Feed forward
back propagation algorithm is 27. Training of the ANN was performed without any allowable
error. The patterns are selected for training and testing the ANN. These selected patterns were
normalized so that they lie between 0 and 1. Twenty Seven patterns were selected for training
the ANN. The inputs and outputs are normalized by,m a x
X
XX
i
i Where Xi is the value of a
feature and Xmax is the maximum value of the feature. A 3-6-1 Feed forward back propagation
network was trained and the structure of the network is shown in figure-4.
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Figure 9 3-6-1 Feed forward back propagation network.
Figure 10 Performance curve of ANN model for 100 epochs
5. VALIDATING THE ARTIFICIAL NEURAL NETWORK
Once the network was trained such that the maximum error for any of the training
data was less than allowable error, the weights and the threshold values were automatically
saved by the program. As the input values from the validation experiments were given to the
ANN program, the program predicts the required output. To validate the results of the Artificial
Neural Network analysis eight data as shown in Table I were used. Once the pH, current
density and temperature are fed into the trained networks, the Coefficient of Friction
Graphite that can be obtained in the Nickel-Graphite composites could be calculated quickly
using ANN model.
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Figure 9 Percentage error of ANN model in the prediction of Friction
Figure 10Closeness of ANN model in the prediction of friction
7. CONCLUSION
A 3-9-1 Feed forward back propagation Artificial Neural Network (ANN) model
was developed for predicting Coefficient of Friction in Nickel-Graphite composite coated metal
matrix using 27 test data .The developed neural net work was validated with eight data. Values
obtained by the above ANN model were compared with the experimental values of the response
variables to decide about the nearness of the predictions with the experimental values.
-10
-8
-6
-4
-2
0
2
46
8
10
1 2 3 4 5 6%o
fError
No.of Experiments
% of Error ANN model
ANN Model
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1 2 3 4 5 6
FrictionforNi-Graphite
Deposit
ion
No. of experiments
Closeness ANN Model prediction
Actual Friction
Measured Friction
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Within the range of input variables for the present case (pH = 3 to 5; current
density (I) = 3 to 5 A/dm2; temperature (T) = 40 to 60
0C), the results showed that Artificial
Neural Network comes in nearness of the predictions to the experimental values of Coefficient
of Friction as the average errors in case of ANN is very less i.e. 0.128484% only.
8.REFERENCES
1 Jack lapinski, derekpletcher, frank c. walsh, The electro deposition of Ni-Graphite
composite layers(2011), Surface and coating technology. Vol. 42, pp.70-73.
2 Haijun Zhao, Lei Liu, Wenbin Hu, Bin ShenFriction and wear behaviour of Nigraphite
composites prepared by electroforming,(2007) Materials& design, Vol. 18, pp.1374-
1378.
3 Didier Floner, Florence Geneste Homogeneous coating of graphite felt by nickel electro
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