The aim of this paper is to introduce a method to forecast the mechanical properties of a composite based on its constitutive materials using a neural network. As input data, a limited number of tests to train the network are needed. From them it will be possible to make forecasts, with a less than 1% of error, the material properties. The forecasts can be done, not only inside the training range but also outside but with an unbounded error rate.
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Mechanical properties forecast in composites using neural networks
Semantic Scholar · Engineering · 2011
Abstract
The aim of this paper is to introduce a method to forecast the mechanical properties of a composite based on its constitutive materials using a neural network. As input data, a limited number of tests to train the network are needed. From them it will be possible to make forecasts, with a less than 1% of error, the material properties. The forecasts can be done, not only inside the training range but also outside but with an unbounded error rate.