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.


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.


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


1. Zhang, Z., et al. (2024). Hardness-guided machine learning for tungsten alloy strength prediction. Materials Today Communications, 39, 109070.

2. Jain, S., Jain, R., Dewangan, S., & Bhowmik, A. (2024). A Machine learning perspective on hardness prediction in advanced multicomponent Al-Mg based lightweight alloys. Materials Letters, 365, 136473.

3. Chai, C.-r., et al. (2024). Machine learning-assisted design of low elastic modulus β-type medical titanium alloys and experimental validation. Computational Materials Science, 238, 112902.

4. Noh, S., et al. (2022). Binary hybrid filler composite formulations of surface modified Fe–Si–Al alloys for multifunctional EMI shielding and thermal conduction. Materials Chemistry and Physics, 284, 126024.

5. Xu, W., Chen, X., Zhu, G., & Pan, F. (2024). Preparation of Mg-6Zn-1Y-0.5 Zr alloy sheet with excellent mechanical properties and electromagnetic interference shielding effectiveness by extrusion plus rolling. Materials Characterization, 207, 113461.

6. Ma, E., Shin, S.-H., Choi, W., Byun, J., & Hwang, B. (2023). Machine learning approach for predicting the fracture toughness of NbSi based alloys. International Journal of Refractory Metals and Hard Materials, 117, 106420.

7. Jiang, L., et al. (2024). Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning. Acta Materialia, 270, 119873.

8. Zhang, X., et al. (2022). High-throughput directed energy deposition-based manufacturing combined with machine learning to fabricate gradient-composition Cu-Fe-Cr alloys. Materials Letters, 308, 131247.

9. Li, J., Cao, B., Chen, H., & Li, L. (2023). Accelerated design of chromium carbide overlays via design of experiment and machine learning. Materials Letters, 333, 133672.

10. Ma, C., Tang, Y., & Bao, G. (2024). Machine learning-based prediction and generation model for creep rupture time of Nickel-based alloys. Computational Materials Science, 233, 112736.

11. Eldabah, N. M., Shoukry, A., Khair-Eldeen, W., Kobayashi, S., & Gepreel, M. A.-H. (2023). Design and characterization of low Young’s modulus Ti-Zr-Nb-based medium entropy alloys assisted by extreme learning machine for biomedical applications. Journal of Alloys and Compounds, 968, 171755.

12. Nelaturu, P., et al. (2024). Multi-principal element alloy discovery using directed energy deposition and machine learning. Materials Science and Engineering: A, 891, 145945.

13. Zadeh, S. H., et al. (2023). An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys. Computational Materials Science, 226, 112225.

14. Wei, B., et al. (2022). Prediction of electrochemical impedance spectroscopy of high-entropy alloys corrosion by using gradient boosting decision tree. Materials Today Communications, 32, 104047.

Data Availability

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

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
ISBN (Print)
ISBN (Online)
Published Date
28 March 2024
Advances in Engineering Innovation
ISSN (Print)
ISSN (Online)
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