Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
To study the long-term creep behavior prediction of polymethacrylimide (PMI) foams, the gradation loading creep tests were proposed at four different temperatures in this paper. The Stepped Isostress (SSM) and TTSP methods were combined to obtain the master curve under reference stress and temperatu...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d6c0973112344a44bad2ec718bce3e07 |
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| Sumario: | To study the long-term creep behavior prediction of polymethacrylimide (PMI) foams, the gradation loading creep tests were proposed at four different temperatures in this paper. The Stepped Isostress (SSM) and TTSP methods were combined to obtain the master curve under reference stress and temperature. The artificial Neural Networks (ANN) technique was used to build the long-term creep behavior prediction model of PMI materials. The effects of different activation functions, hidden layer structures, and other super parameters on the prediction performance were investigated. The results suggest that the SSM plus TTSP method can be used to construct the master curve, which could predict a larger time scale of material creep behavior based on a short-term test. It is of great significance and feasible to predicts the long-term creep life of materials accurately using advanced artificial intelligence technology. According to the statistical analysis, the logistic type activation function has a more accurate and stable prediction performance on long-term creep behavior prediction of PMI. To avoid overfitting, the number of hidden layers should be as small as possible, and the prediction performance of a single hidden layer structure with 8 neurons is sufficient for long-term creep behavior prediction in the engineering area. The statistical value of the correlation coefficient was greater than 0.995. The application range of advanced artificial intelligence technology in this field can be further expanded in the preceding research, such as in the prediction of long-term creep behavior on the composition level of the material. |
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