Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 7, 28 March 2024


Open Access | Article

Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy

Luting Wang 1 , Suiyuan Chen * 2 , Xiancheng Zhu 3 , Zhiqing Fang 4 , Jing Liang 5
1 Northeastern University
2 Northeastern University
3 Northeastern University
4 Northeastern University
5 Northeastern University

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 7, 55-59
Published 28 March 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Luting Wang, Suiyuan Chen, Xiancheng Zhu, Zhiqing Fang, Jing Liang. Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy. AEI (2024) Vol. 7: 55-59. DOI: 10.54254/2977-3903/7/2024075.

Abstract

The compilation and preprocessing of electromagnetic shielding material databases using machine learning techniques are pivotal in contemporary materials science and engineering. These materials play a crucial role in diverse applications such as electronics, telecommunications, aerospace, and automotive industries, necessitating effective attenuation of electromagnetic interference (EMI). This paper underscores the significance of comprehensive databases in organizing material properties systematically, facilitating the identification of novel materials with enhanced shielding effectiveness. It explores the role of machine learning algorithms in predictive modeling and data analysis, expediting the screening process of candidate materials and uncovering hidden correlations within complex datasets. However, the efficacy of machine learning techniques relies heavily on the quality of input data and preprocessing steps. Thus, this paper discusses methodologies for collecting material data, challenges in data curation and integration, and common preprocessing techniques such as data cleaning, feature extraction, and normalization. The integration of electromagnetic shielding material databases with machine learning preprocessing holds great promise for FeCo-based alloy laser cladding manufacturing and design, leading to innovative solutions for electromagnetic interference mitigation across various technological domains.

Keywords

electromagnetic interference shielding effectiveness, machine learning, database, functional materials

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
ISBN (Print)
ISBN (Online)
Published Date
28 March 2024
Series
Advances in Engineering Innovation
ISSN (Print)
2977-3903
ISSN (Online)
2977-3911
DOI
10.54254/2977-3903/7/2024075
Copyright
28 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated