Abstract
In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The fiber volume fraction, fiber length, matrix-phase properties, and fiber orientation are varied so that the neural network can be used within a very wide range of parameters. A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network. The average prediction accuracy is equal or better than by a two-step homogenization using the classical method of Mori and Tanaka. Moreover, it is shown that the training of the neural network on an extended data set works well and that particularly calculation-intensive data points can be avoided without loss of prediction quality.
Highlights
The results of finite element simulations of threedimensional representative volume elements are used as a data basis for the neural network
A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network
The reliable and proper design of composite components consisting of short fiber reinforced plastics (SFRP) is still a big challenge
Summary
The reliable and proper design of composite components consisting of short fiber reinforced plastics (SFRP) is still a big challenge. The modeling of composite properties by neural networks based on RVEs appears to be reasonable. The required computation time of technical components can be significantly reduced compared to a FE2 approach and the results will still be based on detailed RVEs requiring less assumptions compared to homogenization methods like the method of Mori–Tanaka. Several studies have been presented that investigate material properties or material modeling of composite materials using neural networks. Electrical and electrical–mechanical coupled properties are investigated in [20,21] for carbon nanotube composites All these investigations show that neural networks can be used to precisely predict complex material properties if a sufficient database exists. It is investigated to reduce the numerical effort without compromising the quality of the predictions
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