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Prediction of the fatigue life of natural rubber composites by artificial neural network approaches
Highlights An artificial neural network model was established to predict tensile fatigue life of natural rubber composites. A sensitive analytical model was founded to deduce the most critical factor on fatigue property of NR composites. The average predictionaccuracy of artificial neural network is 97.3%. Stress at 100% is the most important factor affecting fatigue life.
Abstract 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%, tensilestrength, elongation at break) and viscoelasticity property (tan δ 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 δ at 7% strain, elongation at break almost the same affecting degree on fatigue life, while tensile strength contributes least.
Prediction of the fatigue life of natural rubber composites by artificial neural network approaches
Highlights An artificial neural network model was established to predict tensile fatigue life of natural rubber composites. A sensitive analytical model was founded to deduce the most critical factor on fatigue property of NR composites. The average predictionaccuracy of artificial neural network is 97.3%. Stress at 100% is the most important factor affecting fatigue life.
Abstract 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%, tensilestrength, elongation at break) and viscoelasticity property (tan δ 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 δ at 7% strain, elongation at break almost the same affecting degree on fatigue life, while tensile strength contributes least.
Prediction of the fatigue life of natural rubber composites by artificial neural network approaches
Xiang, Ke-Lu (author) / Xiang, Pu-Yu (author) / Wu, You-Ping (author)
2013-12-17
6 pages
Article (Journal)
Electronic Resource
English
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