Modeling the young modulus of nanocomposites: A neural network approach

Abstract

Composite materials have changed the way of using polymers, as the strength was favored by the incorporation of fibers and particles. This new class of materials allowed a larger number of applications. The insertion of nanometric sized particles has enhanced the variation of properties with a smaller load of fillers. In this paper, we attempt to a better understanding of nanocomposites by using an artificial intelligence's technique, known as artificial neural networks. This technique allowed the modeling of Young's modulus of nanocomposites. A good approximation was obtained, as the correlation between the data and the response of the network was high, and the error percentage was low.

Artificial neural network technique to predict dynamic fracture of particulate composite

“…Recently, owing to ANN's capability to predict the behaviour of such materials, it has been implemented by many investigators. 20,21 Xu and Gupta 22 and Cupertino et al 23 investigated the applicability of ANN methodology for the prediction of elastic modulus of laminates reinforced with carbon fibres and nanocomposites under static loading, respectively.…”

Dielectric and Mechanical Properties of 3D Printed Nanocomposites with Varied Particle Diameter

“…The compositions of 0.15 volume fraction particles of any diameter yielded composite moduli well over 6 MPa at room temperature, even approaching 50 MPa for the 30 nm samples. This result was quite surprising, as room temperature results in literature at similar volume and weight fractions for varying composite systems show experimental modulus increases of no more than 15 x . − TGA data as shown in Table S5 confirmed minimal variability between the printed samples in particle filler fraction. Figure b clearly shows a modulus increase for the 30 nm sample of over 35 x at just above room temperature 30 °C.…”

Inteligência Computacional Aplicada à Síntese de Semicondutores Quaternários InAlGaAs

“…A utilização de algoritmos de inferência já vem sendo aplicadaà síntese de nanoestruturas. Existem atualmente diversos trabalhos disponíveis na literatura com algoritmos capazes de resolver problema específicos, tais como [3], [4], [5], [6] e [7].…”