Abstract
Predicting the agin life of rubber is mostly based on traditional dynamic methods. These methods often have some limitations, which can not reflect the influence of certain factors. To avoid such limitations, a BP neural network model was established to predict the aging life of rubber. Comparing with the BP neural network model, results from the genetic algorithm optimization model (GA-BP) and the particle swarm optimization model (PSO-BP) showed that the GA-BP network model has better stability and accuracy and can quickly get the global optimal solution. The prediction accuracy of the GA-BP neural network model is better than that of the traditional dynamic model and its result is in good agreement with the experimental data.
Highlights
The aging of rubber is a very common phenomenon
The neural network method can skip the link of establishing the mathematical function, it could learn the abstract network relationship between the input and output based on the experimental data, and there is no limit to the number of the input and output of the model
In view of this this paper proposed a neural network model to predict the aging life of rubber, based on the BP neural network method and algorithm optimization
Summary
The aging of rubber is a very common phenomenon. It is the main form of rubber products failure. The neural network method can skip the link of establishing the mathematical function, it could learn the abstract network relationship between the input and output based on the experimental data, and there is no limit to the number of the input and output of the model In view of this this paper proposed a neural network model to predict the aging life of rubber, based on the BP neural network method and algorithm optimization. Predicting the aging time of the gasket at room temperature (25 °C) when the retention rate of the rubber compression set was reduced to 0.3 by running the neural network model 30 times from 1 to 6 nodes in hidden layer separately, and setting the number of iterations to 100 for each run.
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