Classification, identification, and quantitative study of defects in aluminum plates using pulsed alternating current field measurement combined with principal component analysis

主成分分析 鉴定(生物学) 领域(数学) 材料科学 电流(流体) 组分(热力学) 生物系统 分析化学(期刊) 模式识别(心理学) 计算机科学 人工智能 物理 工程类 化学 色谱法 数学 电气工程 热力学 生物 植物 纯数学
作者
Qingxiao Kong,Shuwei Pan,Lilong Lin,Xinfeng Li
出处
期刊:Frontiers in Materials [Frontiers Media]
卷期号:12
标识
DOI:10.3389/fmats.2025.1569055
摘要

Introduction This study investigates an approach for defect characterization in non-ferromagnetic materials by combining Pulsed Alternating Current Field Measurement (PACFM) with Principal Component Analysis (PCA). The research demonstrates how this integrated method can effectively classify and quantify both surface and subsurface defects through signal processing of PACFM data. Methods The PACFM technique was utilized to acquire defect response signals from non-ferromagnetic specimens. Subsequently, PCA was implemented to decompose the multidimensional PACFM datasets into principal components, with each component preserving the most diagnostically significant information. In this analytical framework, the classification of defects was determined by the sign of the mapped value w 2 in the PCA eigenvector direction, while the magnitude of w 2 exhibited a correlation with subsurface defect burial depths. Results The integrated PACFM-PCA approach successfully discriminated between surface and subsurface defects. The polarity of the principal component w 2 served as a reliable feature for defect classification, with positive values consistently corresponding to subsurface defects and negative values indicating surface defects. Furthermore, a robust quadratic relationship correlation was established between the eigenspace coordinates of subsurface defect signals and their respective burial depths, enabling accurate quantitative assessment of burial depth. Discussion The integration of PACFM with PCA provides a robust framework for defect analysis in non-ferromagnetic materials. This synergistic approach demonstrates significant capability in extracting and quantifying defect signatures from complex response signals, highlighting its considerable potential for non-destructive testing (NDT) applications. Future work could explore its adaptability to more intricate defect geometries.
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