Abstract

An integrated finite element and artificial neural network method is used to analyze the impact of scratch process parameters on some variables related to elastoplastic deformation of titanium alloy. The elastoplastic constitutive parameters applied for scratch simulations are obtained from the nanoindentation experiments and finite element analysis. The validity of the finite element model of scratch is confirmed by comparing the friction forces from simulations to those from experiments. The input parameters of the artificial neural network are three scratch process parameters: tip normal force, tip radius, and shear friction coefficient. The outputs are four variables related to material deformation measured during scratch: scratch depth, elastic recovery height, plowing height, and plowing friction coefficient. The network is trained with pairs of input and output datasets generated by scratch simulations. The prediction results of the neural network are in agreement with the finite element results. The model provides assistance for the prediction and analysis of complex relationships between scratch process parameters and variables related to material deformation, and between the plowing friction coefficient and the relevant parameters. The results show the independence of scratch depth and the shear friction coefficient, and the positive relationships between the shear friction coefficient and plowing friction coefficient.

References

References
1.
Blau
,
P. J.
,
2007
, “
The Significance and Use of the Friction Coefficient
,”
Tribol. Int.
,
34
(
9
), pp.
585
591
. 10.1016/S0301-679X(01)00050-0
2.
Lafaye
,
S.
,
Gauthier
,
C.
, and
Schirrer
,
R.
,
2005
, “
A Surface Flow Line Model of a Scratching Tip: Apparent and True Local Friction Coefficients
,”
Tribol. Int.
,
38
(
2
), pp.
113
127
. 10.1016/j.triboint.2004.06.006
3.
Bowden
,
F. P.
, and
Tabor
,
D.
,
2001
,
The Friction and Lubrication of Solids
, Vol.
1
,
Oxford University Press
,
Oxford
.
4.
Suh
,
N. P.
, and
Sin
,
H. C.
,
1981
, “
The Genesis of Friction
,”
Wear
,
69
(
1
), pp.
91
114
. 10.1016/0043-1648(81)90315-X
5.
Lee
,
K.
,
Marimuthu
,
K. P.
,
Kim
,
C. L.
, and
Lee
,
H.
,
2018
, “
Scratch-Tip-Size Effect and Change of Friction Coefficient in Nano/Micro Scratch Tests Using XFEM
,”
Tribol. Int.
,
120
, pp.
398
410
. 10.1016/j.triboint.2018.01.003
6.
Jiang
,
H.
,
Browning
,
R.
,
Fincher
,
J.
,
Gasbarro
,
A.
,
Jones
,
S.
, and
Sue
,
H. J.
,
2008
, “
Influence of Surface Roughness and Contact Load on Friction Coefficient and Scratch Behavior of Thermoplastic Olefins
,”
Appl. Surf. Sci.
,
254
(
15
), pp.
4494
4499
. 10.1016/j.apsusc.2008.01.067
7.
Goddard
,
J.
, and
Wilman
,
H.
,
1962
, “
A Theory of Friction and Wear During the Abrasion of Metals
,”
Wear
,
5
(
2
), pp.
114
135
. 10.1016/0043-1648(62)90235-1
8.
Mishra
,
M.
,
Egberts
,
P.
,
Bennewitz
,
R.
, and
Szlufarska
,
I.
,
2012
, “
Friction Model for Single-Asperity Elastic-Plastic Contacts
,”
Physica B
,
86
(
4
), pp.
6335
6335
. 10.1103/physrevb.86.045452
9.
Challen
,
J. M.
, and
Oxley
,
P. L. B.
,
1979
, “
An Explanation of the Different Regimes of Friction and Wear Using Asperity Deformation Models
,”
Wear
,
53
(
2
), pp.
229
243
. 10.1016/0043-1648(79)90080-2
10.
Lafaye
,
S.
, and
Troyon
,
M.
,
2006
, “
On the Friction Behaviour in Nanoscratch Testing
,”
Wear
,
261
(
7–8
), pp.
905
913
. 10.1016/j.wear.2006.01.036
11.
Mishra
,
M.
, and
Szlufarska
,
I.
,
2012
, “
Analytical Model for Plowing Friction at Nanoscale
,”
Tribol. Lett.
,
45
(
3
), pp.
417
426
. 10.1007/s11249-011-9899-y
12.
Ichimura
,
H.
, and
Ishii
,
Y.
,
2003
, “
Effects of Indenter Radius on the Critical Load in Scratch Testing
,”
Surf. Coat. Technol.
,
165
(
1
), pp.
1
7
. 10.1016/S0257-8972(02)00718-1
13.
Lafaye
,
S.
,
Gauthier
,
C.
, and
Schirrer
,
R.
,
2006
, “
The Ploughing Friction: Analytical Model with Elastic Recovery for a Conical Tip with a Blunted Spherical Extremity
,”
Tribol. Lett.
,
21
(
2
), pp.
95
99
. 10.1007/s11249-006-9018-7
14.
Jardret
,
V.
,
Zahouani
,
H.
,
Loubet
,
J. L.
, and
Mathia
,
T. G.
,
1998
, “
Understanding and Quantification of Elastic and Plastic Deformation During a Scratch Test
,”
Wear
,
218
(
1
), pp.
8
14
. 10.1016/S0043-1648(98)00200-2
15.
Chowdhury
,
M. A.
, and
Helali
,
M. M.
,
2006
, “
The Effect of Frequency of Vibration and Humidity on the Coefficient of Friction
,”
Tribol. Int.
,
39
(
9
), pp.
958
962
. 10.1016/j.triboint.2005.10.002
16.
Varga
,
M.
,
Leroch
,
S.
,
Rojacz
,
H.
, and
Ripoll
,
M. R.
,
2017
, “
Study of Wear Mechanisms at High Temperature Scratch Testing
,”
Wear
,
388–389
, pp.
112
118
. 10.1016/j.wear.2017.04.027
17.
Reed
,
R.
,
1993
, “
Pruning Algorithms-a Survey
,”
IEEE Trans. Neural Networks
,
4
(
5
), pp.
740
747
. 10.1109/72.248452
18.
D’addona
,
D. M.
, and
Teti
,
R.
,
2013
, “
Genetic Algorithm-Based Optimization of Cutting Parameters in Turning Processes
,”
Procedia CIRP
,
7
, pp.
323
328
. 10.1016/j.procir.2013.05.055
19.
Sardiñas
,
R. Q.
,
Reis
,
P.
, and
Davim
,
J. P.
,
2006
, “
Multi-Objective Optimization of Cutting Parameters for Drilling Laminate Composite Materials by Using Genetic Algorithms
,”
Compos. Sci. Technol.
,
66
(
15
), pp.
3083
3088
. 10.1016/j.compscitech.2006.05.003
20.
Zendehboudi
,
S.
,
Ahmadi
,
M. A.
,
James
,
L.
, and
Chatzis
,
I.
,
2012
, “
Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network With Particle Swarm Optimization
,”
Energ. Fuel
,
26
(
6
), pp.
3432
3447
. 10.1021/ef300443j
21.
Marko
,
H.
,
Simon
,
K.
,
Tomaz
,
I.
,
Matej
,
P.
,
Joze
,
B.
, and
Miran
,
B.
,
2014
, “
Turning Parameters Optimization Using Particle Swarm Optimization
,”
Procedia Eng.
,
69
, pp.
670
677
. 10.1016/j.proeng.2014.03.041
22.
Akay
,
B.
, and
Karaboga
,
D.
,
2012
, “
Artificial Bee Colony Algorithm for Large-Scale Problems and Engineering Design Optimization
,”
J. Intell. Manuf.
,
23
(
4
), pp.
1001
1014
. 10.1007/s10845-010-0393-4
23.
Yildiz
,
A. R.
,
2013
, “
Optimization of Cutting Parameters in Multi-Pass Turning Using Artificial Bee Colony-Based Approach
,”
Inf. Sci.
,
220
, pp.
399
407
. 10.1016/j.ins.2012.07.012
24.
Ritter
,
H.
,
Martinetz
,
T.
, and
Schulten
,
K.
,
1992
,
Neural Computation and Self-Organizing Maps: an Introduction. Computation and Neural Systems Series
,
Addison-Wesley Publishing Company
,
Reading, MA
25.
Hertz
,
J.
,
Krogh
,
A.
, and
Palmer
,
R. G.
,
1991
,
Introduction to the Theory of Neural Computation
,
Addison-Wesley/Addison Wesley Longman
,
Reading, MA
.
26.
Ghaboussi
,
J.
,
Garrett
,
J. H.
, Jr.
, and
Wu
,
X.
,
1991
, “
Knowledge-Based Modeling of Material Behavior with Neural Networks
,”
J. Eng. Mech.
,
117
(
1
), pp.
132
153
. 10.1061/(ASCE)0733-9399(1991)117:1(132)
27.
Ellis
,
G. W.
,
Yao
,
C.
,
Zhao
,
R.
, and
Penumadu
,
D.
,
1995
, “
Stress-Strain Modeling of Sands Using Artificial Neural Networks
,”
J. Geotech. Geoenviron. Eng.
,
121
(
5
), pp.
429
435
. 10.1061/(asce)0733-9410(1995)121:5(429)
28.
Huber
,
N.
, and
Tsakmakis
,
C.
,
1999
, “
Determination of Constitutive Properties From Spherical Indentation Data Using Neural Networks. Part ii: Plasticity with Nonlinear Isotropic and Kinematic Hardening
,”
J. Mech. Phys. Solids
,
47
(
7
), pp.
1589
1607
. 10.1016/S0022-5096(98)00110-0
29.
Tho
,
K. K.
,
Swaddiwudhipong
,
S.
,
Liu
,
Z. S.
, and
Hua
,
J.
,
2004
, “
Artificial Neural Network Model for Material Characterization by Indentation
,”
Model. Simul. Mater. Sci. Eng.
,
12
(
5
), pp.
1055
1062
. 10.1088/0965-0393/12/5/019
30.
Hadzima-Nyarko
,
M.
,
Nyarko
,
E. K.
, and
Morić
,
D.
,
2011
, “
A Neural Network Based Modelling and Sensitivity Analysis of Damage Ratio Coefficient
,”
Expert Syst. Appl.
,
38
(
10
), pp.
13405
13413
. 10.1016/j.eswa.2011.04.169
31.
Ezugwu
,
E. O.
,
Fadare
,
D. A.
,
Bonney
,
J.
,
Da Silva
,
R. B.
, and
Sales
,
W. F.
,
2005
, “
Modelling the Correlation Between Cutting and Process Parameters in High-Speed Machining of Inconel 718 Alloy Using an Artificial Neural Network
,”
Int. J. Mach. Tools Manuf.
,
45
(
12–13
), pp.
1375
1385
. 10.1016/j.ijmachtools.2005.02.004
32.
Hadi
,
M. N.
,
2003
, “
Neural Networks Applications in Concrete Structures
,”
Comput. Struct.
,
81
(
6
), pp.
373
381
. 10.1016/S0045-7949(02)00451-0
33.
Zhao
,
Z.
, and
Ren
,
L.
,
2002
, “
Failure Criterion of Concrete Under Triaxial Stresses Using Neural Networks
,”
Comput.-Aided Civ. Infrastruct. Eng.
,
17
(
1
), pp.
68
73
. 10.1111/1467-8667.00254
34.
Gajdar
,
T.
,
Rudas
,
I.
, and
Suda
,
Y.
,
1997
, “
Neural Network Based Estimation of Friction Coefficient of Wheel and Rail
,”
Proceedings of 1997 IEEE International Conference
,
Budapest, Hungary
,
Sept. 17
.
35.
Senatore
,
A.
,
D’Agostino
,
V.
,
Di Giuda
,
R.
, and
Petrone
,
V.
,
2011
, “
Experimental Investigation and Neural Network Prediction of Brakes and Clutch Material Frictional Behaviour Considering the Sliding Acceleration Influence
,”
Tribol. Int.
,
44
(
10
), pp.
1199
1207
. 10.1016/j.triboint.2011.05.022
36.
Oliver
,
W. C.
, and
Pharr
,
G. M.
,
1992
, “
An Improved Technique for Determining Hardness and Elastic Modulus Using Load and Displacement Sensing Indentation Experiments
,”
J. Mater. Res.
,
7
(
6
), pp.
1564
1583
. 10.1557/JMR.1992.1564
37.
Loubet
,
J. L.
,
Georges
,
J. M.
,
Marchesini
,
O.
, and
Meille
,
G.
,
1984
, “
Vickers Indentation Curves of Magnesium Oxide (MgO)
,”
ASME J. Tribol.
,
106
(
1
), pp.
43
48
. 10.1115/1.3260865
38.
Doerner
,
M. F.
,
Gardner
,
D. S.
, and
Nix
,
W. D.
,
1986
, “
Plastic Properties of Thin Films on Substrates as Measured by Submicron Indentation Hardness and Substrate Curvature Techniques
,”
J. Mater. Res.
,
1
(
6
), pp.
845
851
. 10.1557/JMR.1986.0845
39.
Field
,
J. S.
, and
Swain
,
M. V.
,
1995
, “
Determining the Mechanical Properties of Small Volumes of Material From Submicrometer Spherical Indentations
,”
J. Mater. Res.
,
10
(
1
), pp.
101
112
. 10.1557/JMR.1995.0101
40.
Chevalier
,
S.
,
Clement
,
C.
,
Robledo
,
O.
,
Klein
,
B.
,
Gascan
,
H.
, and
Wijdenes
,
J.
,
1998
, “
Scaling Approach to Conical Indentation in Elastic-Plastic Solids with Work Hardening
,”
J. Appl. Phys.
,
84
(
3
), pp.
1284
1291
. 10.1063/1.368196
41.
Dao
,
M.
,
Chollacoop
,
N.
,
Van Vliet
,
K. J.
,
Venkatesh
,
T. A.
, and
Suresh
,
S.
,
2001
, “
Computational Modeling of the Forward and Reverse Problems in Instrumented Sharp Indentation
,”
Acta Mater.
,
49
(
19
), pp.
3899
3918
. 10.1016/S1359-6454(01)00295-6
42.
Zeng
,
K.
, and
Chiu
,
C. H.
,
2001
, “
An Analysis of Load–Penetration Curves From Instrumented Indentation
,”
Acta Mater.
,
49
(
17
), pp.
3539
3551
. 10.1016/S1359-6454(01)00245-2
43.
Kara
,
F.
,
Aslantaş
,
K.
, and
Çiçek
,
A.
,
2016
, “
Prediction of Cutting Temperature in Orthogonal Machining of AISI 316L Using Artificial Neural Network
,”
Appl. Soft Comput.
,
38
, pp.
64
74
. 10.1016/j.asoc.2015.09.034
44.
Malinov
,
S.
,
Sha
,
W.
, and
McKeown
,
J. J.
,
2001
, “
Modelling the Correlation Between Processing Parameters and Properties in Titanium Alloys Using Artificial Neural Network
,”
Comput. Mater. Sci.
,
21
(
3
), pp.
375
394
. 10.1016/S0927-0256(01)00160-4
45.
Hagan
,
M. T.
, and
Menhaj
,
M. B.
,
1994
, “
Training Feedforward Networks With the Marquardt Algorithm
,”
IEEE Trans. Neural Netw.
,
5
(
6
), pp.
989
993
. 10.1109/72.329697
46.
Hecht-Nielsen
,
R.
,
1989
, “
Theory of the Backpropagation Neural Network
,”
International 1989 Joint Conference on Neural Networks
,
Washington, DC
.
47.
Johnson
,
K. L.
,
1987
,
Contact Mechanics
,
Cambridge University Press
,
Cambridge
.
You do not currently have access to this content.