Abstract
Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the “S-parameters”. In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate−5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism.
Highlights
Since 2019, artificial intelligence (AI) is in the spotlight
This paper is organized as follows: Section 2 sets the problematic and the objective and shows the motivation and the originality of this work; Section 3 presents the materials and data collection used in this study; Section 4 gives a description of the different phases involved in the proposed method, including the background information, implementation details of the evaluation procedure; Section 5 introduces the Rozanov formalism used for gauging the absorption performance; Section 6 presents the experimental part in which the results are discussed; Section 7 closes the article with conclusions and future work
We investigated the use of AI in the electromagnetic interference (EMI) shielding field by developing a new system based on a multilayer perceptron (MLP) neural network (NN) method, designed to predict the electromagnetic absorption of PC/CNT composites films reinforced by different weight percentage of carbon nanotubes
Summary
Since 2019, artificial intelligence (AI) is in the spotlight. This technology is developing at high speed, and its use cases are increasing in all sectors. The development of a new numerical method consisting of a multilayer perceptron neural network, aiming to predict the electromagnetic absorption index at an identified frequency, is designed to surpass the above issues This method has been chosen due to the large and different scale of input data as it is presented in Table 1 in materials and data collection section; MLP has been shown to approximate virtually any function to any desired accuracy; this is validated only if the number of training data in the series is sufficiently large, which acquire a good understanding for the MLP network to learn the required input-output relationship accurately [14]. This paper is organized as follows: Section 2 sets the problematic and the objective and shows the motivation and the originality of this work; Section 3 presents the materials and data collection used in this study; Section 4 gives a description of the different phases involved in the proposed method, including the background information, implementation details of the evaluation procedure; Section 5 introduces the Rozanov formalism used for gauging the absorption performance; Section 6 presents the experimental part in which the results are discussed; Section 7 closes the article with conclusions and future work
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