FeSiAl系合金吸波剂的机器学习设计及其多相协同磁共振

孙易泽; 王旭; 廖晨曦; 柯亚娇; 陈志宏

doi:10.19554/j.cnki.1001-3563.2026.13.038

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包装工程（技术栏目） ›› 2026, Vol. 47 ›› Issue (13) : 359-371. DOI: 10.19554/j.cnki.1001-3563.2026.13.038

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FeSiAl系合金吸波剂的机器学习设计及其多相协同磁共振

孙易泽¹, 王旭¹, 廖晨曦², 柯亚娇^1*, 陈志宏¹

作者信息 +

Machine Learning Design of FeSiAl-based Alloy Microwave Absorbents and Their Multiphase Synergistic Magnetic Resonance

SUN Yize¹, WANG Xu¹, LIAO Chenxi², KE Yajiao^1*, CHEN Zhihong¹

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文章历史 +

摘要

目的针对传统FeSiAl系软磁合金低频磁导率差、多组元掺杂原子特异性占位不确定以及多相协同共振行为不明确的问题,通过机器学习成分优化与先进表征技术,从原子尺度到介观尺度解构FeSiAl系软磁合金低频宽带磁损耗机理。方法融合了物理启发特征与Robust-XGBoost模型进行高通量筛选与预测,获得最佳元素组分,经气雾化、高能球磨与真空热处理制备片状粉末。运用同步辐射X射线吸收谱和GSAS-Ⅱ全谱精修技术精确解析微观原子占位;并结合微磁学动力学仿真,量化计算A2、B2与DO3相的本征铁磁共振响应。结果围绕高磁导率和低介电目标,通过机器学习预测出Fe_82.5Si_9.6Al_5.4Cu_1.1Ni_1.4新型五元合金吸波剂。制备的新合金吸波剂在600 ℃热处理后发生了无序A2相向B2及DO3有序相转变,形成质量比约为34∶26∶41的三相共存态。其中,Cu元素脱溶形成弥散超小团簇;Ni原子主要位于DO3相棱心位置。仿真证实,A2、B2与DO3相的本征自然共振频率分别为3.60、2.86与0.98 GHz,进一步与畴壁共振的线性叠加,形成了非对称低频宽带磁共振,显著提升了低频性能。结论通过机器学习可定制化实现多元合金吸波剂成分设计,且特异性原子占位与多相协同磁共振可显著拓宽低频吸收带宽,为新型低频吸波材料设计提供新思路。

Abstract

The work aims to address the poor low-frequency permeability, the uncertain specific occupation of multi-component doped atoms, and the ambiguous multi-phase synergistic resonance behavior of traditional FeSiAl-based soft magnetic alloys suffer by unraveling the low-frequency broadband magnetic loss mechanism from atomic to mesoscopic scales via machine learning-based composition optimization and advanced characterization techniques. Physics-informed features were integrated with the Robust-XGBoost model for high-throughput screening and prediction to determine the optimal elemental composition, and flake-shaped powders were subsequently fabricated by gas atomization, high-energy ball milling and vacuum heat treatment. Synchrotron radiation X-ray absorption spectroscopy and GSAS-Ⅱ whole pattern refinement technology were adopted to precisely resolve the microscopic atomic occupation. Combined with micromagnetic dynamic simulation, the intrinsic ferromagnetic resonance responses of A2, B2 and DO3 phases were quantitatively calculated. Targeting high magnetic permeability and low dielectric constant, a novel quinary alloy microwave absorbent of Fe_82.5Si_9.6Al_5.4Cu_1.1Ni_1.4 was designed and predicted by machine learning. After vacuum heat treatment at 600 ℃, a phase transition from disordered A2 phase to ordered B2 and DO3 phases occurred in the as-prepared alloy, forming a triple-phase coexisting state with a mass ratio of approximately 34:26:41. In this system, Cu atoms precipitated to form dispersed ultra-fine clusters, while Ni atoms mainly occupied the edge-center sites of the DO3 phase. Simulation results verified that the intrinsic natural resonance frequencies of A2, B2 and DO3 phases were 3.60, 2.86 and 0.98 GHz, respectively. The linear superposition of these intrinsic resonances and domain wall resonance contributed to the formation of asymmetric low-frequency broadband magnetic resonance, which remarkably enhanced the low-frequency electromagnetic performance. Machine learning enables the customized composition design of multi-alloy microwave absorbents. Moreover, the specific atomic occupation and multi-phase synergistic magnetic resonance can effectively broaden the low-frequency absorption bandwidth, providing a novel insight for the design of advanced low-frequency microwave absorbing materials.

导出引用

孙易泽, 王旭, 廖晨曦, 柯亚娇, 陈志宏. FeSiAl系合金吸波剂的机器学习设计及其多相协同磁共振[J]. 包装工程. 2026, 47(13): 359-371 https://doi.org/10.19554/j.cnki.1001-3563.2026.13.038

SUN Yize, WANG Xu, LIAO Chenxi, KE Yajiao, CHEN Zhihong. Machine Learning Design of FeSiAl-based Alloy Microwave Absorbents and Their Multiphase Synergistic Magnetic Resonance[J]. Packaging Engineering. 2026, 47(13): 359-371 https://doi.org/10.19554/j.cnki.1001-3563.2026.13.038

中图分类号： TB34

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