Abstract
Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters.
Highlights
Polyethylene is extensively used as an insulated material in electrical and electronic applications due to its high dielectric properties
2a’,b’), can nanocomposites better observe the differences in same clay dispersion polymer magnification
The results show that the tensile modulus and tensile strength for with compatibilizer increased in all nanoclay composition and decreased
Summary
Polyethylene is extensively used as an insulated material in electrical and electronic applications due to its high dielectric properties. The exceptional mechanical properties of nanoclay fillers, namely, high tensile modulus and tensile strength, make them a potential candidate for the enhancement of the mechanical properties of some polymer matrices. This improvement is due to the high contact between the clay platelets and the polymer [12,13,14]. The deep neural network (DNN) approach has been widely used in many applications, including speech, digit and face recognition; form and object detection; and experiment electrical andDNN thermal properties of used nanocomposites [18,19,20,21,22,23,24,25,26,27,28,29,30].
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