Preparation methods for Graphene, Metal and Polymer-based Composites for EMI Shielding Materials: State of the Art of Conventional and Machine Learning Methods

Ayub, Saba; Guan, Beh Hoe; Ahmad, Faiz; Javed, Muhammad Faisal; Mosavi, Amir; Felde, Imre

doi:10.20944/preprints202107.0299.v1

Cited by 8 publications

(2 citation statements)

References 70 publications

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“…For instance, the concentration of graphene, density of porous materials, amount of filler content and the thickness of the composite impacts an improvement in the shielding effectiveness. Before proceeding towards the method adaptation, it is important to have an in-dept understanding of the properties of the materials as well, as the authors discussed in [24]. Various studies showed that by increasing the graphene loading and composite thickness, at some extent the shielding effectiveness increases.…”

Section: Methods For Preparation Of Polymer-based Compositesmentioning

confidence: 99%

“…This review aims to assess the various traditional and artificial intelligence methods to synthesize the shielding composites to deal with EMI. This paper builds on a previous review [24] conducted on the applications of graphene, iron and polymer composites in EMI shielding, where the top materials were highlighted in each frequency range to secure good shielding effectiveness. In this review preparation methods that help to build the EMI shielding composites have been reviewed.…”

Section: Introductionmentioning

confidence: 94%

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Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Ayub

Guan

Ahmad

et al. 2021

Metals

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Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance.

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Section: Methods For Preparation Of Polymer-based Compositesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Ayub

Guan

Ahmad

et al. 2021

Metals

Self Cite

View full text Add to dashboard Cite

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Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength

et al. 2022

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Solid-state welding is a derivative of the friction stir spot welding (FSSW) technique, which has been developed as a new method for joining aluminum alloys. FSSW is a variant of linear friction stir welding intended to deal with lightweight alloy resistance spot welding (RSW) and riveting. Tensile strength refers to a material’s ability to withstand excessive stress when being stretched or pulled before necking; it is expressed in terms of force per unit area. The tensile strength in stir spot welding is affected by dynamic and static parameters. The control of dynamic parameters and static parameters is studied in this paper to optimize the friction stir spot welding strength. A fuzzy logic control system is used to optimize the process as a new approach that can be used in this field. The obtained results prove that the fuzzy logic control system is an easy and inexpensive technology that can be used in prediction and optimization for the strength of FSSW. Furthermore, the results show the efficacy and adequacy of the proposed fuzzy logic control system.

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Artificial Neural Networks Based Optimization Techniques: A Review

et al. 2021

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In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

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Preparation methods for Graphene, Metal and Polymer-based Composites for EMI Shielding Materials: State of the Art of Conventional and Machine Learning Methods

Cited by 8 publications

References 70 publications

Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength

Artificial Neural Networks Based Optimization Techniques: A Review

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