In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium matrix
composite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites with
four different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were produced
in different sintering times. The specimens were characterized using metallographic observation,
microhardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned conditions,
a multi layer perceptron back propagation feed forward neural network was constructed to evaluate
and compare the experimental calculated data to predicted values. In neural network training modules,
different composition, sintering time and reinforcement size were used as input (3 inputs), hardness and
Ultimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using the
prepared training set. At the end of training process the test data were used to check the system’s accuracy.
As a result, the comparison of neural network output results with the results from experiments and
empirical relationship has shown good agreement with average error of 2.5%.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3511

Abdoos, H.

Amirjan, M.

Khorsand, H.

SocioEconomic Challenges Journal (Sumy State University)

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE NANOMATERIALS: APPLICATIONS AND PROPERTIES 
Vol. 1 No 3, 03CNN16(4pp) (2012) 
 
 
2304-1862/2012/1(3)03CNN16(4) 03CNN16-1  2012 Sumy State University 
Investigation of Manufacturing Parameters on the Mechanical Properties of Powder  
Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks 
 
M. Amirjan* H. Khorsand, H. Abdoos 
 
Material Division, Faculty of Mechanical Engineering, K.N. Toosi University of Technology 
 
(Received 25 June 2012; published online 24 August 2012) 
 
In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium ma-
trix composite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites with 
four different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were pro-
duced in different sintering times. The specimens were characterized using metallographic observation, 
microhardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned condi-
tions, a multi layer perceptron back propagation feed forward neural network was constructed to evaluate 
and compare the experimental calculated data to predicted values. In neural network training modules, 
different composition, sintering time and reinforcement size were used as input (3 inputs), hardness and 
Ultimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using the 
prepared training set. At the end of training process the test data were used to check the system’s accura-
cy. As a result, the comparison of neural network output results with the results from experiments and 
empirical relationship has shown good agreement with average error of 2.5%. 
 
Keywords: Mg-Al2O3 Nanocomposite, Artificial neural network, Hardness, UTS, Powder metallurgy.  
 
 PACS numbers: 46.55. +d, 62.20.Qp 
 
 
                                                          
* mamirjan@yahoo.com 
1. INTRODUCTION  
 
Metal Matrix Composites (MMCs) have been widely 
recognized to have relatively superior mechanical prop-
erties, such as better wear resistance, higher elastic 
modulus and yield strength, as compared to the unrein-
forced monolithic metal. As compared to fiber rein-
forced MMCs, particulate reinforced MMCs are gaining 
popularity due to their ease of fabrication, high 
throughput and lower manufacturing cost [1]. Among 
the various types of MMCs, light-weight MMCs such as 
magnesium (Mg) based composites are arousing more 
interest due to their potential applications in aero-
space, automotive and sports equipment industries. 
With a judicious selection of particulate reinforce-
ments, magnesium based composites are known to have 
high specific mechanical properties [2], low density, 
improved thermal and dimensional stability and better 
damping properties.  
 Al2O3 short fibers and particulates are commonly 
used as reinforcement for magnesium and have been 
shown to improve the tensile strength of magnesium 
alloys [3,4] and thermal stability of the grain structure 
in pure magnesium [5]. Additionally, recent studies on 
reinforcing pure magnesium with sub-micron and 
nano-size Al2O3 particulates have shown promising 
results with simultaneous increment observed in both 
strength and ductility of magnesium using both solidi-
fication and powder metallurgy techniques [6,7]. 
 Artificial Neural Networks (ANNs) have been 
emerged as a new branch of computing, suitable for 
application in a wide range of fields. A lot of studies 
have been published in which the prediction of several 
composites properties [8-10]. 
ANNs are based on the neural structure of the hu-
man brain, which processes information between many 
neurons and in the past few years there has been a 
constant increase in interest of neural network model-
ing in different field of materials science [9-12]. The 
basic unit in ANNs is the neuron. The neurons are con-
nected to each other with weight factor that determines 
the strength of the inter connections and thus the con-
tribution of that interconnection to the following neu-
rons. ANNs can be trained to perform a particular 
function by adjusting the values of these weight factors 
between the neurons either from the information from 
outside the network or by the neuron themselves in 
response to input. This is the key to ability of ANNs to 
achieve learning and memory. 
The multilayered neural Network (MLP) is the most 
widely applied to neural network which has been used 
in most researches so far[7]. A back propagation algo-
rithm can be used to train these multilayer feed for-
ward networks with differentiable transfer function to 
approximation, pattern association and pattern classi-
fication. The term back propagation refers to the pro-
cess by which derivatives of network error, with respect 
to network weight and biases can be computed. The 
training of ANNs by back propagation involves these 
stage: 
 The feed forward of the input training pattern 
 The calculation and back propagation of the 
associated error 
 The adjustment of weights 
This process can be used with a number of different 
optimization strategies. In present study, a MLP neu-
ral network was used to prediction and confirmation of 
experimental results of microhardness and UTS of Mg-
Al2O3 composites. 
 
2. EXPRIMENTAL PROCEDURE  
 
Magnesium powder (average size of 50 m) and 
Al2O3 particles with four different size of 50 nm, 
200 nm, 1 m and 50 m were used for production of 
Mg-Al2O3 nanocomposites. Also, several weight percent 
 M. AMIRJAN H. KHORSAND, H. ABDOOS PROC. NAP 1, 03CNN16 (2012) 
 
 
03CNN16-2 
of Al2O3 reinforcement (0.25, 0.5, 1 and 1.5%) were 
used. The powder mixture were mechanically alloyed in 
a ball mill in 15 hr and with ball to powder ratio (BPR) 
of 1:10. Then the composite powders were consolidated 
to green parts. Sintering process was carried out at 
610 C at several sintering time (15, 30, 60, 90, 120, 
150 min.) in reducing atmosphere. 
Hardness and Ultimate Tensile strength(UTS) 
measurements were performed to evaluating the prop-
erties of these composites. 
A Back Propagation Algorithm was used for model-
ing and prediction of results with ANNs .In this model-
ing process the composite composition, sintering time 
and reinforcement size were used as input and hard-
ness and UTS were recorded as output parameters in 
ANNs design. MLP architecture and training parame-
ters were presented in Table 1 and ANNs block dia-
gram given at Fig. 1. 
 
Table 1 – Multilayer perceptron architecture and training 
parameters 
 
The number of neurons on the 
layers 
Input:3,Hidden:10, 
Output: 2 
The initial weights and biases 
Randomly be-
tween -1 to 1 
Activation functions for hidden 
and output layers 
Log Sigmoid 
Training parameters learning 
rule 
Back Propagation 
Adaptive learning rate of hid-
den/output layer 
0.2 
Number of iteration 15000 
Momentum constant 0.5 
Acceptable mean squared error 0.001 
 
 
 
Fig. 1 – ANNs Block diagram in this study 
 
3. RESULTS AND DISCUSSIONS 
 
3.1 The Effect of Sintering Time on the Mechan-
ical Properties  
 
Fig. 2, Fig. 3 and Table 2, Table 3 are shown the ef-
fect of processing parameters consisting sintering time 
and Al2O3 reinforcement(amount and size) on the micro-
hardness and UTS of composites. As seen, in every group 
of composites, in a given sintering temperature of 61 C, 
hardness and UTS increases as the sintering time in-
creases from 15 to 90 min and then show a decrease in 
hardness from 90 to 15 min. During sintering process in 
addition to consolidation and bonding of particles in 
structure, the recrystallization and growth occurs in mi-
crostructure, as the sintering time increases there is 
enough time for growth of nucleated grains and coarsen-
ing. Therefore, coarse grain structure will be occur with 
increased sintering time.  
 
 
 
Fig. 2 – The effect of sintering time on the microhardness of 
Mg-Al2O3 composite with nano size reinforcement 
 
 
 
Fig. 3 – The effect of sintering time on the UTS of Mg-Al2O3 
composite with nano size reinforcement 
 
3.2 The effect of amount and Reinforcement size 
on the Mechanical properties 
 
As it is see in Fig. 4 and Fig. 5, in a given sintering 
time, hardness and UTS increase as the amount of re-
inforcement from 0.25 to 1.5% wt. Also, hardness and 
UTS of composites decrease as the size of reinforcement 
increase from nanosize to micron level. Higher values 
of microhardness and UTS observed for the composite 
with 1.5wt % of 50 nm reinforcement at sintering time 
of 90 min. 
The increase in hardness of the magnesium matrix 
with the addition of nano-size reinforcements can be 
attributed primarily to the: (i) presence of harder na-
nopowder reinforcement in the matrix and (ii) higher 
constraint to the localized matrix deformation due to 
the presence of harder phases. These results are con-
sistent with the trend observed by other investigators 
[13-15]. The increase in UTS can be attributed to the 
combined influence of: (i) work hardening due to the 
strain misfit between the reinforcing particulates and 
the matrix, (ii) the formation of internal thermal 
stresses due to different thermal expansion behavior 
between Al2O3 reinforcement and the matrix, (iii) Oro-
wan strengthening and (iv) reduction in grain size. Oro-
wan strengthening due to the presence of sub-micron and 
nano-size particulates has been shown to contribute to the 
improvement in the yield strength of particulate rein-
forced metal matrix composites [14-17]. 
Experimental 
Experimental 
 INVESTIGATION OF MANUFACTURING PARAMETERS ON THE MECHANICAL… PROC. NAP 1, 03CNN16 (2012) 
 
 
03CNN16-3 
Table 2 – The effect of processing parameters on the microhardness and UTS values of composites(Experimental and Predicted Values) 
 
Comp. 
Sintering 
Time(Min.) 
Reinforcement 
size(µm) 
Microhardness(HV) UTS(MPa) 
Experimental 
Values 
Predicted 
Values 
%Error 
Experimental 
Values 
Predicted 
Values 
%Error 
M
g
-
0
.2
5
A
l 2
O
3
 
90 
0.05 48.234 47.893 -0.712 175.450 177.034 0.894743 
0.20 43.623 43.004 -1.4394 168.679 168.123 -0.33071 
1.00 40.033 38.984 -2.69085 159.467 158.943 -0.32968 
50.00 36.970 36.000 -2.69444 151.349 152.496 0.752151 
M
g
-
0
.5
0
A
l 2
O
3
 
90 
0.05 54.023 55.012 1.79779 180.289 181.338 0.578478 
0.20 49.738 48.234 -3.11813 173.945 174.034 0.051139 
1.00 42.546 43.258 1.645938 165.345 165.007 -0.20484 
50.00 39.134 38.945 -0.4853 160.223 159.582 -0.40167 
M
g
-
1
.0
0
A
l 2
O
3
 
90 
0.05 60.695 61.485 1.284866 213.556 212.421 -0.53432 
0.20 52.078 53.674 2.973507 203.669 202.497 -0.57877 
1.00 47.893 46.896 -2.12598 195.234 196.329 0.557737 
50.00 42.356 41.557 -1.92266 189.492 191.056 0.818608 
M
g
-
1
.5
0
A
l 2
O
3
 
90 
0.05 68.945 69.456 0.735718 209.168 208.228 -0.45143 
0.20 62.567 61.789 -1.25912 200.005 201.834 0.90619 
1.00 56.355 55.006 -2.45246 191.407 190.491 -0.48086 
50.00 51.667 53.045 2.597794 185.038 186.330 0.693393 
 
Mathematically, the contribution to yield strength by 
Orowan strengthening can be expressed as [18]: 
 
 
0.4 .
ln / 1orowan Mg
G B d
M
d
 
 
where 2 / 3d d , Mg is the Poisson’s ratio for Mg and 
 is the mean inter-particle distance given by 
/ 4 1d f .  
The increase in strength of the Mg/Al2O3 composites 
can be partly attributable to the reduction in grain size. 
The refinement in grain size arises due to the presence of 
reinforcing particles which acts as nucleation sites during 
recrystallization and the pinning of grain boundaries by 
the particles resulting in limited grain growth. 
 
 
 
Fig. 4 – The effect of Reinforcement size on the microhardness of 
composites at sintering time of 90min 
 
In this study, prediction of Microhardness and UTS of 
Mg-Al2O3 MMC were performed using a back propagation  
neural network. These experimental results have been 
compared with ANNS results. Iteration number has been 
selected 15000. Three input neurons, 10 neurons in in-
termediate layers and 2 output neurons [3:10:2] have been 
selected for this study. The learning rate and momentum 
values have been selected as 0.2 and 0.5, respectively. 
 
 
Fig. 5 – The effect of Reinforcement size on the UTS of composites 
at sintering time of 90 min 
 
4. CONCLUSIONS 
 
In present study, prediction of Mg-Al2O3 compo-
sites under several processing conditions was per-
formed. Following results were obtained: 
 Sintering time and reinforcement size and 
amount were used as input while the microhardness 
and UTS were the output of the model. These data 
were obtained from experimental work. 
 Fig. 6 and Fig. 7 shows the predicted values 
of microhardness and UTS, respectively. Microhard-
ness and UTS of specimens have shown a consistency 
with predicted results. Theses trained values had an 
average error of 2.5% in Microhardness and 1.5% in 
UTS values. 
 Artificial Neural Network (ANNS) can be used 
as efficient tool in predicting composite properties. Un-
der given condition and prescribed materials predicted 
values of properties can be utilized by designers and 
process engineers and account as a cost saving item in 
process.  
 Experimental microhardness and UTS of spec-
imens have shown a consistency and good agreement 
with predicted results of ANNs model.  
 In this study, designed ANN model was pre-
dicted the microhardness with an average error of 2.5% 
and UTS with about 1.5%. 
 M. AMIRJAN H. KHORSAND, H. ABDOOS PROC. NAP 1, 03CNN16 (2012) 
 
 
03CNN16-4 
 
 
Fig. 6 – The Effect of sintering time on the microhardness of 
Mg-Al2O3 composite with nano size reinforcement (Predicted) 
 
 
 
Fig. 7 – The Effect of sintering time on the UTS of Mg-Al2O3 
composite with nano size reinforcement(Predicted) 
 
 
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Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks

Electronic Sumy State University Institutional Repository

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE NANOMATERIALS: APPLICATIONS AND PROPERTIES Vol. 1 No 3, 03CNN16(4pp) (2012)   2304-1862/2012/1(3)03CNN16(4) 03CNN16-1  2012 Sumy State University Investigation of Manufacturing Parameters on the Mechanical Properties of Powder  Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks  M. Amirjan* H. Khorsand, H. Abdoos  Material Division, Faculty of Mechanical Engineering, K.N. Toosi University of Technology  (Received 25 June 2012; published online 24 August 2012)  In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium ma-trix composite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites with four different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were pro-duced in different sintering times. The specimens were characterized using metallographic observation, microhardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned condi-tions, a multi layer perceptron back propagation feed forward neural network was constructed to evaluate and compare the experimental calculated data to predicted values. In neural network training modules, different composition, sintering time and reinforcement size were used as input (3 inputs), hardness and Ultimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using the prepared training set. At the end of training process the test data were used to check the system’s accura-cy. As a result, the comparison of neural network output results with the results from experiments and empirical relationship has shown good agreement with average error of 2.5%.  Keywords: Mg-Al2O3 Nanocomposite, Artificial neural network, Hardness, UTS, Powder metallurgy.    PACS numbers: 46.55. +d, 62.20.Qp                                                             * mamirjan@yahoo.com 1. INTRODUCTION   Metal Matrix Composites (MMCs) have been widely recognized to have relatively superior mechanical prop-erties, such as better wear resistance, higher elastic modulus and yield strength, as compared to the unrein-forced monolithic metal. As compared to fiber rein-forced MMCs, particulate reinforced MMCs are gaining popularity due to their ease of fabrication, high throughput and lower manufacturing cost [1]. Among the various types of MMCs, light-weight MMCs such as magnesium (Mg) based composites are arousing more interest due to their potential applications in aero-space, automotive and sports equipment industries. With a judicious selection of particulate reinforce-ments, magnesium based composites are known to have high specific mechanical properties [2], low density, improved thermal and dimensional stability and better damping properties.   Al2O3 short fibers and particulates are commonly used as reinforcement for magnesium and have been shown to improve the tensile strength of magnesium alloys [3,4] and thermal stability of the grain structure in pure magnesium [5]. Additionally, recent studies on reinforcing pure magnesium with sub-micron and nano-size Al2O3 particulates have shown promising results with simultaneous increment observed in both strength and ductility of magnesium using both solidi-fication and powder metallurgy techniques [6,7].  Artificial Neural Networks (ANNs) have been emerged as a new branch of computing, suitable for application in a wide range of fields. A lot of studies have been published in which the prediction of several composites properties [8-10]. ANNs are based on the neural structure of the hu-man brain, which processes information between many neurons and in the past few years there has been a constant increase in interest of neural network model-ing in different field of materials science [9-12]. The basic unit in ANNs is the neuron. The neurons are con-nected to each other with weight factor that determines the strength of the inter connections and thus the con-tribution of that interconnection to the following neu-rons. ANNs can be trained to perform a particular function by adjusting the values of these weight factors between the neurons either from the information from outside the network or by the neuron themselves in response to input. This is the key to ability of ANNs to achieve learning and memory. The multilayered neural Network (MLP) is the most widely applied to neural network which has been used in most researches so far[7]. A back propagation algo-rithm can be used to train these multilayer feed for-ward networks with differentiable transfer function to approximation, pattern association and pattern classi-fication. The term back propagation refers to the pro-cess by which derivatives of network error, with respect to network weight and biases can be computed. The training of ANNs by back propagation involves these stage:  The feed forward of the input training pattern  The calculation and back propagation of the associated error  The adjustment of weights This process can be used with a number of different optimization strategies. In present study, a MLP neu-ral network was used to prediction and confirmation of experimental results of microhardness and UTS of Mg-Al2O3 composites.  2. EXPRIMENTAL PROCEDURE   Magnesium powder (average size of 50 m) and Al2O3 particles with four different size of 50 nm, 200 nm, 1 m and 50 m were used for production of Mg-Al2O3 nanocomposites. Also, several weight percent  M. AMIRJAN H. KHORSAND, H. ABDOOS PROC. NAP 1, 03CNN16 (2012)   03CNN16-2 of Al2O3 reinforcement (0.25, 0.5, 1 and 1.5%) were used. The powder mixture were mechanically alloyed in a ball mill in 15 hr and with ball to powder ratio (BPR) of 1:10. Then the composite powders were consolidated to green parts. Sintering process was carried out at 610 C at several sintering time (15, 30, 60, 90, 120, 150 min.) in reducing atmosphere. Hardness and Ultimate Tensile strength(UTS) measurements were performed to evaluating the prop-erties of these composites. A Back Propagation Algorithm was used for model-ing and prediction of results with ANNs .In this model-ing process the composite composition, sintering time and reinforcement size were used as input and hard-ness and UTS were recorded as output parameters in ANNs design. MLP architecture and training parame-ters were presented in Table 1 and ANNs block dia-gram given at Fig. 1.  Table 1 – Multilayer perceptron architecture and training parameters  The number of neurons on the layers Input:3,Hidden:10, Output: 2 The initial weights and biases Randomly be-tween -1 to 1 Activation functions for hidden and output layers Log Sigmoid Training parameters learning rule Back Propagation Adaptive learning rate of hid-den/output layer 0.2 Number of iteration 15000 Momentum constant 0.5 Acceptable mean squared error 0.001    Fig. 1 – ANNs Block diagram in this study  3. RESULTS AND DISCUSSIONS  3.1 The Effect of Sintering Time on the Mechan-ical Properties   Fig. 2, Fig. 3 and Table 2, Table 3 are shown the ef-fect of processing parameters consisting sintering time and Al2O3 reinforcement(amount and size) on the micro-hardness and UTS of composites. As seen, in every group of composites, in a given sintering temperature of 61 C, hardness and UTS increases as the sintering time in-creases from 15 to 90 min and then show a decrease in hardness from 90 to 15 min. During sintering process in addition to consolidation and bonding of particles in structure, the recrystallization and growth occurs in mi-crostructure, as the sintering time increases there is enough time for growth of nucleated grains and coarsen-ing. Therefore, coarse grain structure will be occur with increased sintering time.     Fig. 2 – The effect of sintering time on the microhardness of Mg-Al2O3 composite with nano size reinforcement    Fig. 3 – The effect of sintering time on the UTS of Mg-Al2O3 composite with nano size reinforcement  3.2 The effect of amount and Reinforcement size on the Mechanical properties  As it is see in Fig. 4 and Fig. 5, in a given sintering time, hardness and UTS increase as the amount of re-inforcement from 0.25 to 1.5% wt. Also, hardness and UTS of composites decrease as the size of reinforcement increase from nanosize to micron level. Higher values of microhardness and UTS observed for the composite with 1.5wt % of 50 nm reinforcement at sintering time of 90 min. The increase in hardness of the magnesium matrix with the addition of nano-size reinforcements can be attributed primarily to the: (i) presence of harder na-nopowder reinforcement in the matrix and (ii) higher constraint to the localized matrix deformation due to the presence of harder phases. These results are con-sistent with the trend observed by other investigators [13-15]. The increase in UTS can be attributed to the combined influence of: (i) work hardening due to the strain misfit between the reinforcing particulates and the matrix, (ii) the formation of internal thermal stresses due to different thermal expansion behavior between Al2O3 reinforcement and the matrix, (iii) Oro-wan strengthening and (iv) reduction in grain size. Oro-wan strengthening due to the presence of sub-micron and nano-size particulates has been shown to contribute to the improvement in the yield strength of particulate rein-forced metal matrix composites [14-17]. Experimental Experimental  INVESTIGATION OF MANUFACTURING PARAMETERS ON THE MECHANICAL… PROC. NAP 1, 03CNN16 (2012)   03CNN16-3 Table 2 – The effect of processing parameters on the microhardness and UTS values of composites(Experimental and Predicted Values)  Comp. Sintering Time(Min.) Reinforcement size(µm) Microhardness(HV) UTS(MPa) Experimental Values Predicted Values %Error Experimental Values Predicted Values %Error Mg-0.25Al 2O3 90 0.05 48.234 47.893 -0.712 175.450 177.034 0.894743 0.20 43.623 43.004 -1.4394 168.679 168.123 -0.33071 1.00 40.033 38.984 -2.69085 159.467 158.943 -0.32968 50.00 36.970 36.000 -2.69444 151.349 152.496 0.752151 Mg-0.50Al 2O3 90 0.05 54.023 55.012 1.79779 180.289 181.338 0.578478 0.20 49.738 48.234 -3.11813 173.945 174.034 0.051139 1.00 42.546 43.258 1.645938 165.345 165.007 -0.20484 50.00 39.134 38.945 -0.4853 160.223 159.582 -0.40167 Mg-1.00Al 2O3 90 0.05 60.695 61.485 1.284866 213.556 212.421 -0.53432 0.20 52.078 53.674 2.973507 203.669 202.497 -0.57877 1.00 47.893 46.896 -2.12598 195.234 196.329 0.557737 50.00 42.356 41.557 -1.92266 189.492 191.056 0.818608 Mg-1.50Al 2O3 90 0.05 68.945 69.456 0.735718 209.168 208.228 -0.45143 0.20 62.567 61.789 -1.25912 200.005 201.834 0.90619 1.00 56.355 55.006 -2.45246 191.407 190.491 -0.48086 50.00 51.667 53.045 2.597794 185.038 186.330 0.693393  Mathematically, the contribution to yield strength by Orowan strengthening can be expressed as [18]:   0.4 .ln / 1orowan MgG B dMd  where 2 / 3d d , Mg is the Poisson’s ratio for Mg and  is the mean inter-particle distance given by / 4 1d f .  The increase in strength of the Mg/Al2O3 composites can be partly attributable to the reduction in grain size. The refinement in grain size arises due to the presence of reinforcing particles which acts as nucleation sites during recrystallization and the pinning of grain boundaries by the particles resulting in limited grain growth.    Fig. 4 – The effect of Reinforcement size on the microhardness of composites at sintering time of 90min  In this study, prediction of Microhardness and UTS of Mg-Al2O3 MMC were performed using a back propagation  neural network. These experimental results have been compared with ANNS results. Iteration number has been selected 15000. Three input neurons, 10 neurons in in-termediate layers and 2 output neurons [3:10:2] have been selected for this study. The learning rate and momentum values have been selected as 0.2 and 0.5, respectively.   Fig. 5 – The effect of Reinforcement size on the UTS of composites at sintering time of 90 min  4. CONCLUSIONS  In present study, prediction of Mg-Al2O3 compo-sites under several processing conditions was per-formed. Following results were obtained:  Sintering time and reinforcement size and amount were used as input while the microhardness and UTS were the output of the model. These data were obtained from experimental work.  Fig. 6 and Fig. 7 shows the predicted values of microhardness and UTS, respectively. Microhard-ness and UTS of specimens have shown a consistency with predicted results. Theses trained values had an average error of 2.5% in Microhardness and 1.5% in UTS values.  Artificial Neural Network (ANNS) can be used as efficient tool in predicting composite properties. Un-der given condition and prescribed materials predicted values of properties can be utilized by designers and process engineers and account as a cost saving item in process.   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Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks

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