Natural rubber life estimation through an extreme learning machine
2022-01-0297
03/29/2022
- Event
- Content
- In engineering applications, rubber isolators are subjected to continuous alternating loads, resulting in fatigue failure. Although some theoretical models are used for the fatigue life estimation of rubber materials, they do not comprehensively consider the influences of multiple factors. In the present study, a model based on the extreme learning machine (ELM) is established to estimate fatigue life of natural rubber (NR) specimens. The mechanical load (engineering strain peak), ambient temperature (23℃, 60℃ and 90℃) and shore hardness (N45 and N50) of NR specimens are used as the input variables while the measure average fatigue life as the output variable of the ELM. The regression results and predicted life distribution of the established ELM model are encouraging. For comparison, the back propagation neural network (BPNN) model and the support vector machine (SVM) model are also implemented. It is concluded that the ELM model is superior to the other two ML models in accuracy and efficiency. The ELM model provides an effective means for accurately predicting the fatigue life of NR.
- Citation
- Liu, X., "Natural rubber life estimation through an extreme learning machine," SAE Technical Paper 2022-01-0297, 2022, .