T.Louie Frango et al., International Journal of Advanced Engineering Technology, E-ISSN 0976-3945
Research Paper
ARTIFICIAL NEURAL NETWORK (ANN) MODELING FOR
PREDICTING HARDNESS OF NI-CBN COMPOSITE COATINGS
T.LouieFrango1, K.Ramanathan2, G.N.K.RameshBapu3, P.Marimuthu4
Address for Correspondence
1
Assistant Professor, Department of Mechanical Engineering, Mahath Amma Institute of Engineering and Technology,
Pudukkottai-622101.
2
Assistant Professor, Department of Mechanical Engineering, A. C. College of Engineering &Technology, Karaikudi-4.
3
Senior Principal Scientist, Central Electrochemical Research Institute, Karaikudi, 630 006.
4
Principal & Professor, Department of Mechanical Engineering, Syed Ammal Engineering College, Ramanathapuram623502,
ABSTRACT
Nickel-CBN 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 CBN powder of 68 m size was used in this study. The volume fraction of CBN deposition in composite coated specimens was measured
gravimetrically. An Artificial Neural Network (ANN) model was developed using 27 practical data obtained by designing an
experiment with three level of experiment namely Low, Medium and High to predict the harness of CBN deposition in NiCBN 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 hardness of Ni-CBN composite coatings.
KEYWORDS: Hardness value (HV), ANN model, Ni-CBN composite coatings, MATLAB
1. INTRODUCTION
Particle-reinforced metal matrix composites generally
exhibit wide engineering applications due to their
enhanced hardness, wear and corrosion resistance
compared to pure metal or alloy [1]. Composite
electroplating has been identified to be a
technologically feasible and economically superior
technique for the preparation of such kind of
composites. Cubic boron nitride (CBN), a man-made
material, is known to be the second hardest material
to diamond. Because of their outstanding properties,
diamond and CBN compacts have been used in the
tool industry for machining applications.
Because of its excellent mechanical and electrical
properties, CBN is of great interest for a number of
applications (e.g. grinding powders, wear parts,
electronic parts, etc.). Searching the literature, the
impression arises that the applications of CBN 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 CBN powder in
the
Ni-CBN
composite
coating
measured
gravimetrically was earlier demonstrated [2]. The
amount of CBN deposited in the composite metal
matrix is mainly affected by the process parameters
such as current density, pH value, temperature of the
bath solution and concentration of CBN dispersed in
the electrolyte. Volume fraction of CBN influences
the hardness of Ni-CBN composite coatings, hence it
is essential to develop a prediction model for
estimating the hardness of Nickel-CBN composite
using the above parameters.
2. 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 [3]. 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 hardness of Ni-CBN in the
deposit for the given condition. Figure.3 shows the
architecture of a standard supervised training FFBP
ANN and Figure 2 shows the perception.
Figure 1. ANN Model with two hidden layers
Figure 2. A typical processing element (Perception).
Int J Adv Engg Tech/Vol. VII/Issue II/April-June,2016/1234-1237
T.Louie Frango et al., International Journal of Advanced Engineering Technology, E-ISSN 0976-3945
Figure 3. Standard supervised training feed forward
neural network
ANN approach is applied in many fields. Egon
Eckert et al [7] applied this tool in the field of
chemical engineering for modeling of pyrolysis,
utilizing the characterization of atmospheric gas oil
based on incomplete data. N. Daneshvar et al [8] used
ANN for modeling of decolorization of textile dye
solution. Modeling capability of the artificial neural
network (ANN) to predict the effect of the hot
deformation parameters on the strength of Al-base
metal matrix composites was carried by Issam S.
Jalham [9] in metal forming area. Joseph H.W. Lee et
al [10] carried out neural network modeling to predict
the algal blooms dynamics of coastal water. For
predicting the yield strength, tensile strength and
elongation of cast alloys, Mehmet Sirac Ozerdem et
al [11] developed ANN modeling and validated the
model. ANN model to predict the oxides of nitrogen
(NOx) emissions from diesel engine under various
operating variables was performed by obodeh et al
[12]. Reza soleymany et al [13] optimized the coating
variables of physical vapour deposition and chemical
vapour deposition process for achieving the
maximum hardness of titanium film. Subramanian et
al [13] has developed regression model and ANN
model for estimating the deposition rate of copper-tin
during electroplating. From the literatures it is
evident that ANN is a viable and more useful
modeling tool and it is used almost in all fields such
as engineering, biology etc. This paper deals about
using such a powerful tool for predicting the hardness
of
Nickel-CBN
composite
developed
by
electroplating technique. Most important coating
variables such as current density, pH and bath
temperature were considered as inputs for the ANN
model.
3. EXPERIMENTAL PROCEDURE
3.1 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 in the conventional manner
for removal of organic and inorganic impurities [4].
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.
3.2. 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
7.5×2.5 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. CBN powder (6 to 8 μm) was added to the
electrolyte in the form of slurry. The solution was
stirred using a magnetic stirrer. Stirring was given
initially for 30s to bring all the CBN 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
deposit thickness of 25-40 μm.
3.3. Ni-CBN deposition:
Natural grade polycrystalline CBN powder of 6–8
μm sizes was used. Prior to the co-deposition, the
CBN particles were ultrasonically dispersed in the
bath for 10 min. Experiments were conducted at a
fixed CBN concentration of 10 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 = 3 – 5 A/dm2 ; pH value = 3 –
5; Temperature = 40 to 60 0C .For the prediction of
hardness of CBN 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 Ni-CBN composite coatings were
obtained and their hardness was measured
gravimetrically.
4. 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 hardness of CBN
deposition in Ni-CBN composite matrix. The features
current density, pH and temperature are the inputs
and the hardness of CBN 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,
X i Where Xi is the value of a feature and Xmax
X 
i
X max
is the maximum value of the feature. A 3-7-8-1 Feed
forward back propagation network was trained and
the network is shown in figure-4.
Int J Adv Engg Tech/Vol. VII/Issue II/April-June,2016/1234-1237
T.Louie Frango et al., International Journal of Advanced Engineering Technology, E-ISSN 0976-3945
Figure 4. 3-7-8-1 Feed forward back propagation network.
Figure 5. 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 hardness of CBN that can be obtained
in the Nickel-CBN composites could be calculated
quickly using ANN model
6. RESULTS AND DISCUSSION
A comparison of measured hardness value and
predicted hardness value using the prediction
technique Artificial Neural Network (ANN) is
presented in the Table -II.
The Average Absolute Error of the trained ANN
model is found to be only 0.06141 % and it is shown
graphically in Figure.6. The closeness of ANN model
in predicting the hardness of CBN deposition with
actual value is shown in Figure 7. It is shown clearly
that the prediction technique ANN is better in
predicting the Volume fraction of CBN deposition in
the Nickel-CBN composite coated specimens.
TABLE –I. EXPERIMENTAL COATING PARAMETERS AND HARDNESS VALUE.
Test
No
pH
1
2
3
4
5
6
7
8
3.5
3.5
3.5
3.5
4.5
4.5
4.5
4.5
Current
density
A/dm2
Temperature
0
C
Hardness
value
(VHN)
3.5
3.5
3.5
3.5
4.5
4.5
4.5
4.5
45
55
45
55
45
55
45
55
486.67
500.98
503.71
512.78
468.49
484.98
487.75
498.34
TABLE – II COMPARISON OF MEASURED & PREDICTED HARDNESS OF NI-CBN DEPOSITION
Test No
Measured VHN
1
2
3
4
5
6
7
8
486.67
500.98
503.71
512.78
468.49
484.98
487.75
498.34
Predicted VHN
(ANN model)
486.89
500.56
502.09
513.63
468.05
484.26
486.08
499.77
Figure 6. Percentage error of ANN model in the
prediction of VHN
Int J Adv Engg Tech/Vol. VII/Issue II/April-June,2016/1234-1237
Percentage Error
(ANN model)
-0.0452
0.0838
0.321
-0.1658
0.0939
0.1485
0.342
-0.2869
Figure 7. Closeness of ANN model in predicting
Volume fraction of CBN deposition.
T.Louie Frango et al., International Journal of Advanced Engineering Technology, E-ISSN 0976-3945
7. CONCLUSION
A 3-7-8-1 Feed forward back propagation Artificial
Neural Network (ANN) model was developed for
predicting hardness of CBN in Ni-CBN composite
coated metal matrix using 27 test data. The developed
neural network 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.
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 results showed
that Artificial Neural Network comes in nearness of
the predictions to the experimental values of hardness
of CBN as the average errors in case of ANN is very
less i.e. 0.06141 % only.
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