Copper-based surface composite dispersed with varying fractions of hybrid reinforcement was fabricated through friction stir processing (FSP). Hybrid reinforcement particles were prepared from aluminum nitride (AIN) and boron nitride (BN) particles of equal weight proportion. Based on design of experiments, wear characteristics of the developed copper surface composites were estimated using pin-on-disk tribometer. Experimental parameters include volumetric fraction of hybrid reinforcement particles (5, 10, and 15 vol %), load (10, 20, 30 N), sliding velocity (1, 1.5, and 2 m/s), and sliding distance (500, 1000, and 1500 m). Microstructural characterization demonstrated uniform dispersion of hybrid reinforcement particles onto the copper surface along with good bonding. Hardness of the developed surface composites increased with respect to increase in hybrid particle dispersion when compared with copper substrate while a reduction in density values was revealed. Analysis on wear rate values proved that wear rate decreased with increase in hybrid particle dispersion and increased with increase in load, sliding velocity, and distance. Analysis of variance (ANOVA) specified load as the most significant factor over wear rate values followed by volume fractions of particle dispersion, sliding velocity, and distance. Regression model constructed was found efficient in predicting wear rate values. Analysis of worn out surfaces through scanning electron microscopy (SEM) revealed the transition of severe to mild wear with respect to increase in hybrid reinforcement particle dispersion. A feed forward back propagation algorithm-based artificial neural network (ANN) model with topology 4-7-1 was developed to predict wear rate of copper surface composites based on its control factors.

References

References
1.
Čadek
,
J.
,
Kuchařová
,
K.
, and
Milička
,
K.
,
2004
, “
Creep in Copper Dispersion Strengthened With Fine Alumina Particles and Reinforced With Alumina Short Fibres—An ODS Copper Matrix Composite
,”
J. Alloys Compd.
,
378
(
1–2
), pp.
123
126
.
2.
Zhang
,
J.
,
He
,
L.
, and
Zhou
,
Y.
,
2009
, “
Highly Conductive and Strengthened Copper Matrix Composite Reinforced by Zr2Al3C4 Particulates
,”
Scr. Mater.
,
60
(
11
), pp.
976
979
.
3.
You
,
J. H.
, and
Bolt
,
H.
,
2002
, “
Prediction of Plastic Deformation of Fiber-Reinforced Copper Matrix Composites
,”
J. Nucl. Mater.
,
307–311
, pp. 74–78.
4.
Khosravi
,
J.
,
Givi
,
M. K. B.
,
Barmouz
,
M.
, and
Rahi
,
A.
,
2014
, “
Microstructural, Mechanical, and Thermophysical Characterization of Cu/WC Composite Layers Fabricated Via Friction Stir Processing
,”
Int. J. Adv. Manuf. Technol.
,
74
(
5–8
), pp.
1087
1096
.
5.
Sapate
,
S. G.
,
Uttarwar
,
A.
,
Rathod
,
R. C.
, and
Paretkar
,
R. K.
,
2009
, “
Analyzing Dry Sliding Wear Behaviour of Copper Matrix Composites Reinforced With Pre-Coated SiCp Particles
,”
Mater. Des.
,
30
(
2
), pp.
376
386
.
6.
Shehata
,
F.
,
Fathy
,
A.
,
Abdelhameed
,
M.
, and
Moustafa
,
S. F.
,
2009
, “
Preparation and Properties of Al2O3 Nanoparticle Reinforced Copper Matrix Composites by In Situ Processing
,”
Mater. Des.
,
30
(
7
), pp.
2756
2762
.
7.
Kumar
,
N. M.
,
Senthil Kumaran
,
S.
, and
Kumaraswamidhas
,
L. A.
,
2016
, “
High Temperature Investigation on EDM Process of Al 2618 Alloy Reinforced With Si3N4, ALN and ZrB2 In-Situ Composites
,”
J. Alloys Compd.
,
663
, pp.
755
768
.
8.
Hosseini
,
S. A.
,
Ranjbar
,
K.
,
Dehmolaei
,
R.
, and
Amirani
,
A. R.
,
2015
, “
Fabrication of Al5083 Surface Composites Reinforced by CNTs and Cerium Oxide Nano Particles Via Friction Stir Processing
,”
J. Alloys Compd.
,
622
, pp.
725
733
.
9.
Sathiskumar
,
R.
,
Murugan
,
N.
,
Dinaharan
,
I.
, and
Vijay
,
S. J.
,
2013
, “
Characterization of Boron Carbide Particulate Reinforced In Situ Copper Surface Composites Synthesized Using Friction Stir Processing
,”
Mater. Charact.
,
84
, pp.
16
27
.
10.
Yang
,
M.
,
Xu
,
C.
,
Wu
,
C.
,
Lin
,
K.-C.
,
Chao
,
Y. J.
, and
An
,
L.
,
2010
, “
Fabrication of AA6061/Al2O3 Nano Ceramic Particle Reinforced Composite Coating by Using Friction Stir Processing
,”
J. Mater. Sci.
,
45
(
16
), pp.
4431
4438
.
11.
Sahraeinejad
,
S.
,
Izadi
,
H.
,
Haghshenas
,
M.
, and
Gerlich
,
A. P.
,
2015
, “
Fabrication of Metal Matrix Composites by Friction Stir Processing With Different Particles and Processing Parameters
,”
Mater. Sci. Eng. A
,
626
, pp.
505
513
.
12.
Liu
,
Q.
,
Ke
,
L.
,
Liu
,
F.
,
Huang
,
C.
, and
Xing
,
L.
, 2013, “
Microstructure and Mechanical Property of Multi-Walled Carbon Nanotubes Reinforced Aluminum Matrix Composites Fabricated by Friction Stir Processing
,”
Mater. Des.
,
45
, pp.
343
348
.
13.
Sathiskumar
,
R.
,
Murugan
,
N.
,
Dinaharan
,
I.
, and
Vijay
,
S. J.
,
2014
, “Prediction of Mechanical and Wear Properties of Copper Surface Composites Fabricated Using Friction Stir Processing,”
Mater. Des.
,
55
, pp. 224–234.
14.
Jafari
,
J.
,
Givi
,
M. K. B.
, and
Barmouz
,
M.
,
2015
, “
Mechanical and Microstructural Characterization of Cu/CNT Nanocomposite Layers Fabricated Via Friction Stir Processing
,”
Int. J. Adv. Manuf. Technol.
,
78
(
1–4
), pp.
199
209
.
15.
Leal
,
R. M.
,
Galvão
,
I.
,
Loureiro
,
A.
, and
Rodrigues
,
D. M.
,
2015
, “
Effect of Friction Stir Processing Parameters on the Microstructural and Electrical Properties of Copper
,”
Int. J. Adv. Manuf. Technol.
,
80
(
9–12
), pp.
1655
1663
.
16.
Sathiskumar
,
R.
,
Murugan
,
N.
,
Dinaharan
,
I.
, and
Vijay
,
S. J.
,
2013
, “
Role of Friction Stir Processing Parameters on Microstructure and Microhardness of Boron Carbide Particulate Reinforced Copper Surface Composites
,”
Sadhana
,
38
(6), pp.
1433
1450
.
17.
Thankachan
,
T.
,
Soorya Prakash
,
K.
, and
Kavimani
,
V.
,
2017
, “
Investigations on the Effect of Friction Stir Processing on Cu-BN Surface Composites
,”
Mater. Manuf. Processes
, epub.
18.
Liu
,
Y. Q.
,
Cong
,
H. T.
,
Wang
,
W.
,
Sun
,
C. H.
, and
Cheng
,
H. M.
,
2009
, “
AlN Nanoparticle-Reinforced Nanocrystalline Al Matrix Composites: Fabrication and Mechanical Properties
,”
Mater. Sci. Eng. A
,
505
(
1–2
), pp.
151
156
.
19.
Tajika
,
M.
,
Matsubara
,
H.
, and
Rafaniello
,
W.
,
1999
, “
Microstructural Development in AlN Composite Ceramics
,”
Nanostruct. Mater.
,
12
(
99
), pp.
131
134
.
20.
Chen
,
C.
,
Leichen
,
G.
,
Ji
,
L.
,
Junjie
,
H.
,
Zhimeng
,
G.
, and
Volinsky
,
A. A.
,
2015
, “
Aluminum Powder Size and Microstructure Effects on Properties of Boron Nitride Reinforced Aluminum Matrix Composites Fabricated by Semi-Solid Powder Metallurgy
,”
Mater. Sci. Eng. A
,
646
, pp. 306–314.
21.
Parucker
,
M. L.
,
Klein
,
A. N.
,
Binder
,
C.
,
Ristow
,
W.
, Jr.
, and
Binder
,
R.
,
2014
, “
Development of Self-Lubricating Composite Materials of Nickel With Molybdenum Disulfide, Graphite and Hexagonal Boron Nitride Processed by Powder Metallurgy: Preliminary Study
,”
Mater. Res.
,
17
, pp.
180
185
.
22.
Sangeetha
,
S.
, and
Paruthimal Kalaignan
,
G.
,
2015
, “
Tribological and Electrochemical Corrosion Behavior of Ni–W/BN (Hexagonal) Nano-Composite Coatings
,”
Ceram. Int.
,
41
(9), pp. 10415–10424.
23.
Lorenzo-Martin
,
M. C.
,
2014
, “
Surface Layer Modification of 6061 Al Alloy by Friction Stir Processing and Second Phase Hard Particles for Improved Friction and Wear Performance
,”
ASME J. Tribol.
,
136
(4), p. 044501.
24.
Thankachan
,
T.
, and
Soorya Prakash
,
K.
,
2017
, “
Microstructural, Mechanical and Tribological Behavior of Aluminum Nitride Reinforced Copper Surface Composites Fabricated Through Friction Stir Processing Route
,”
Mater. Sci. Eng. A
,
688
, pp.
301
308
.
25.
Sathiskumar
,
R.
,
Murugan
,
N.
,
Dinaharan
,
I.
, and
Vijay
,
S. J.
,
2014
, “
Fabrication and Characterization of Cu/B4C Surface Dispersion Strengthened Composite Using Friction Stir Processing
,”
Arch. Metall. Mater.
,
59
(1), pp.
83
87
.
26.
Sathiskumar
,
R.
,
Murugan
,
N.
,
Dinaharan
,
I.
, and
Vijay
,
S. J.
,
2011
, “
Effect of Traverse Speed on Microstructure and Microhardness of Cu/B4C Surface Composite Produced by Friction Stir Processing
,”
Trans. Indian Inst. Met.
,
66
(4), pp.
333
337
.
27.
Barmouz
,
M.
,
Besharati Givi
,
M. K.
, and
Seyfi
,
J.
,
2011
, “
On the Role of Processing Parameters in Producing Cu/SiC Metal Matrix Composites Via Friction Stir Processing
,”
Mater. Charact.
,
62
(1), pp.
108
117
.
28.
Radhika
,
N.
, and
Raghu
,
R.
,
2017
, “
Investigation on Mechanical Properties and Analysis of Dry Sliding Wear Behavior of Al LM13/AlN Metal Matrix Composite Based on Taguchi's Technique
,”
ASME J. Tribol.
,
139
(4), p. 041602.
29.
Prakash
,
K. S.
,
Thankachan
,
T.
, and
Radhakrishnan
,
R.
, 2017, “
Parametric Optimization of Dry Sliding Wear Loss of Copper–MWCNT Composites
,”
Trans. Nonferrous Met. Soc. China
,
27
(3), pp.
627
637
.
30.
Yao
,
Y. T.
,
Jiang
,
L.
,
Fu
,
G. F.
, and
Chen
,
L. Q.
,
2015
, “
Wear Behavior and Mechanism of B4C Reinforced Mg-Matrix Composites Fabricated by Metal-Assisted Pressureless Infiltration Technique
,”
Trans. Nonferrous Met. Soc. China
,
25
(8), pp.
2543
2548
.
31.
Azimzadegan
,
T.
,
Khoeini
,
M.
,
Etaat
,
M.
, and
Khoshakhlagh
,
A.
,
2013
, “
An Artificial Neural-Network Model for Impact Properties in X70 Pipeline Steels
,”
Neural Comput. Appl.
,
23
(
5
), pp.
1473
1480
.
32.
Khalaj
,
G.
,
Azimzadegan
,
T.
,
Khoeini
,
M.
, and
Etaat
,
M.
,
2013
, “
Artificial Neural Networks Application to Predict the Ultimate Tensile Strength of X70 Pipeline Steels
,”
Neural Comput. Appl.
,
23
(
7–8
), pp.
2301
2308
.
33.
Khalaj
,
G.
,
Yoozbashizadeh
,
H.
,
Khodabandeh
,
A.
, and
Nazari
,
A.
,
2013
, “
Artificial Neural Network to Predict the Effect of Heat Treatments on Vickers Microhardness of Low-Carbon Nb Microalloyed Steels
,”
Neural Comput. Appl.
,
22
(
5
), pp.
879
888
.
34.
Khalaj
,
G.
,
2013
, “
Artificial Neural Network to Predict the Effects of Coating Parameters on Layer Thickness of Chromium Carbonitride Coating on Pre-Nitrided Steels
,”
Neural Comput. Appl.
,
23
(
3–4
), pp.
779
786
.
35.
Haque
,
M. E.
,
2001
, “
Prediction of Corrosion–Fatigue Behavior of DP Steel Through Artificial Neural Network
,”
Int. J. Fatigue
,
23
(1), pp.
1
4
.
36.
Mirzadeh
,
H.
, and
Najafizadeh
,
A.
,
2008
, “
Correlation Between Processing Parameters and Strain-Induced Martensitic Transformation in Cold Worked AISI 301 Stainless Steel
,”
Mater. Charact.
,
59
(11), pp.
1650
1654
.
37.
Hornik
,
K.
,
Stinchcombe
,
M.
, and
White
,
H.
,
1989
, “
Multilayer Feedforward Networks Are Universal Approximators
,”
Neural Networks
,
2
(5), pp.
359
366
.
You do not currently have access to this content.