Damage mechanism identification in composites via machine learning and acoustic emission

被引：79
|
作者：
Muir, C. [1 ]
Swaminathan, B. [1 ]
Almansour, A. S. [2 ]
Sevener, K. [3 ]
Smith, C. [2 ]
Presby, M. [2 ]
Kiser, J. D. [2 ]
Pollock, T. M. [1 ]
Daly, S. [4 ]
机构：
[1] Univ Calif Santa Barbara, Mat Dept, Santa Barbara, CA USA
[2] NASA, Glenn Res Ctr, Cleveland, OH USA
[3] Univ Michigan, Mat Sci & Engn Dept, Ann Arbor, MI 48109 USA
[4] Univ Calif Santa Barbara, Dept Mech Engn, Santa Barbara, CA 93106 USA
来源：
NPJ COMPUTATIONAL MATERIALS | 2021年 / 7卷 / 01期
基金：
美国国家科学基金会;
关键词：
PATTERN-RECOGNITION APPROACH; FIBER-REINFORCED COMPOSITE; CERAMIC-MATRIX COMPOSITES; HILBERT-HUANG TRANSFORM; SELF-ORGANIZING MAP; FAILURE MODES; WAVELET TRANSFORM; CLUSTER-ANALYSIS; TENSILE TESTS; K-MEANS;
D O I：
10.1038/s41524-021-00565-x
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution. This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling, and error analysis strategies. Fundamental requirements for damage mechanism identification in any machine learning framework, including those currently in use, under development, and yet to be explored, are discussed.
引用
收藏
页数：15
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