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Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy
- 1 Northeastern University
- 2 Northeastern University
- 3 Northeastern University
- 4 Northeastern University
- 5 Northeastern University
* Author to whom correspondence should be addressed.
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
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Cite this article
Wang,L.;Chen,S.;Zhu,X.;Fang,Z.;Liang,J. (2024). Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy. Advances in Engineering Innovation,7,55-59.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal：Advances in Engineering Innovation
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