期刊
MATERIALS & DESIGN
卷 57, 期 -, 页码 180-185出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2013.12.044
关键词
-
资金
- Program for New Century Excellent Talents in University, Ministry of Education of China [NCET-10-0202]
- National Natural Science Foundation of China [51073009]
A back-propagation artificial neural network (BP-ANN) model was established to predict fatigue property of natural rubber (NR) composites. The mechanical properties (stress at 100%, tensile strength, elongation at break) and viscoelasticity property (tan delta at 7% strain) of natural rubber composites were utilized as the input vectors while fatigue property (tensile fatigue life) as the output vector of the BP-ANN. The average prediction accuracy of the established ANN was 97.3%. Moreover, the sensitivity matrixes of the input vectors were calculated to analyze the varied affecting degrees of mechanical properties and viscoelasticity on fatigue property. Sensitivity analysis indicated that stress at 100% is the most important factor, and tan delta at 7% strain, elongation at break almost the same affecting degree on fatigue life, while tensile strength contributes least. (C) 2014 Elsevier Ltd. All rights reserved.
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