Machine learning for the design and prediction of soft-magnetic electromagnetic shielding FeCo-based alloys in laser cladding
Abstract
Electromagnetic shielding materials play a pivotal role in both aerospace applications and daily life. However, their design and manufacturing still face persistent challenges. Machine learning demonstrates significant potential in accelerating material development and compositions optimization. Furthermore, laser additive manufacturing provides powerful technical support for fabricating multi-component, multifunctional electromagnetic shielding materials with tailored properties. In this study, the multiple machine learning strategies have been proposed, based on experimental derivation and soft magnetic material databases, to accelerate the design of multifunctional FeCo-based alloys for electromagnetic interference (EMI) shielding within an almost infinite compositional space. This work presents a novel approach for the rapid and automated discovery of multifunctional alloys with optimized EMI shielding effectiveness, as well as enhanced magnetic and electrical properties.
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BibTeX
@misc{wang2026machine,
title = {{Machine learning for the design and prediction of soft-magnetic electromagnetic shielding FeCo-based alloys in laser cladding}},
author = {Luting Wang and Suiyuan Chen and Xiancheng Zhu and Zhiqing Fang and Mei Wang and Yifan Li and Lei Shen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06417},
eprint = {2609.06417},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06417},
}
RIS
TY - UNPB
TI - Machine learning for the design and prediction of soft-magnetic electromagnetic shielding FeCo-based alloys in laser cladding
AU - Luting Wang
AU - Suiyuan Chen
AU - Xiancheng Zhu
AU - Zhiqing Fang
AU - Mei Wang
AU - Yifan Li
AU - Lei Shen
PY - 2026
DA - 2026/09/06
PB - arXiv
DO - 10.48550/arxiv.2609.06417
UR - https://arxiv.org/abs/2609.06417
AB - Electromagnetic shielding materials play a pivotal role in both aerospace applications and daily life. However, their design and manufacturing still face persistent challenges. Machine learning demonstrates significant potential in accelerating material development and compositions optimization. Furthermore, laser additive manufacturing provides powerful technical support for fabricating multi-component, multifunctional electromagnetic shielding materials with tailored properties. In this study, the multiple machine learning strategies have been proposed, based on experimental derivation and soft magnetic material databases, to accelerate the design of multifunctional FeCo-based alloys for electromagnetic interference (EMI) shielding within an almost infinite compositional space. This work presents a novel approach for the rapid and automated discovery of multifunctional alloys with optimized EMI shielding effectiveness, as well as enhanced magnetic and electrical properties.
ER -