Polynomial classifiers (PC) have already been shown to produce good fatigue life prediction for a specific composite under a variety of fatigue loading conditions. In this study, polynomial classifiers are used to predict the fatigue life in other composite materials not used in training. Different composite materials with a variety of fiber orientation angles are considered. The predictions obtained using PC are compared with the experimental results and are shown to be promising.

1.
El Kadi
,
H.
, 2006, “
Modeling the Mechanical Behavior of Fiber-Reinforced Polymeric Composite Materials Using Artificial Neural Networks—A Review
,”
Compos. Struct.
0263-8223,
73
, pp.
1
23
.
2.
Zhang
,
Z.
, and
Friedrich
,
K.
, 2003, “
Artificial Neural Networks Applied to Polymer Composites: A Review
,”
Compos. Sci. Technol.
0266-3538,
63
, pp.
2029
2044
.
3.
Al-Assaf
,
Y.
, and
El Kadi
,
H.
, 2007, “
Fatigue Life Prediction of Composite Materials Using Polynomial Classifiers and Recurrent Neural Networks
,”
Compos. Struct.
0263-8223,
77
, pp.
561
569
.
4.
Hashin
,
Z.
, and
Rotem
,
A.
, 1973, “
A Fatigue Failure Criterion for Fiber Reinforced Materials
,”
J. Compos. Mater.
0021-9983,
7
, pp.
448
464
.
5.
Awerbuch
,
J.
, and
Hahn
,
H. T.
, 1981,
Off-Axis Fatigue of Graphite/Epoxy Composites in Fatigue of Fibrous Composite Materials
, ASTM STP 723,
American Society for Testing and Materials
,
Philadelphia, PA
, pp.
243
273
.
6.
El Kadi
,
H.
, and
Ellyin
,
F.
, 1994, “
Effect of Stress Ratio on the Fatigue of Unidirectional Glass Fibre/Epoxy Composite Laminae
,”
Composites
0010-4361,
25
, pp.
917
924
.
7.
Philippidis
,
T. P.
, and
Vassilopoulos
,
A. P.
, 2002, “
Complex Stress State Effect on Fatigue Life of GRP Laminates. Part I, Experimental
,”
Int. J. Fatigue
0142-1123,
24
, pp.
813
823
.
8.
Kawai
,
M.
, and
Suda
,
H.
, 2004, “
Effects of Non-Negative Mean Stress on the Off-Axis Fatigue Behavior of Unidirectional Carbon/Epoxy Composites at Room Temperature
,”
J. Compos. Mater.
0021-9983,
38
, pp.
833
854
.
9.
Epaarachchi
,
J. A.
, and
Clausen
,
P. D.
, 2003, “
An Empirical Model for Fatigue Behavior Prediction of Glass Fiber-Reinforced Plastic Composites for Various Stress Ratios and Test Frequencies
,”
Composites, Part A: Appl. Sci. Manuf.
1359-835X,
34
, pp.
313
326
.
10.
Harris
,
B.
,
Reiter
,
H.
,
Adam
,
T.
,
Dickson
,
R. F.
, and
Fernando
,
G.
, 1990, “
Fatigue Behaviour of Carbon Fibre Reinforced Plastics
,”
Composites
0010-4361,
21
, pp.
232
242
.
12.
Al-Assadi
,
M.
, 2009, “
Predicting the Fatigue Failure of Fiber Reinforced Composite Materials Using Artificial Neural Networks
,” MS thesis, American University of Sharjah, Sharjah, UAE.
13.
Abu-Kheil
,
Y.
, 2009, “
System Identification Using Group Method of Data Handling (GMDH)
,” MS thesis, American University of Sharjah, Sharjah, UAE.
You do not currently have access to this content.