Experimental Investigations of Damage Identification for Aluminum Foam Sandwich Beams Using Two-Step Method

被引:0
|
作者
He X. [1 ]
Ge D. [2 ]
An Y. [3 ]
机构
[1] School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, Jiangsu
[2] Secondary Specialized School of Nanjing-Pukou, Nanjing, Jiangsu
[3] School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing, Jiangsu
关键词
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I
10.1155/2023/6551830
中图分类号
学科分类号
摘要
In the experiment, strain gauges and dynamic signal acquisition instruments are used to collect and record data, and the stochastic subspace algorithm is used to extract the first three strain modal parameters of each case. The damage amount identified by the second natural frequency based on the modified Timoshenko beam theory is more in line with the actual situation. The damage depth of case 2 and case 4 is 2 mm, and the identified damage amount is 10% and 9%, respectively. The damage depth of case 3 and case 5 is 4 mm, and the identified damage amount is 16% and 23%, respectively. The damage location information of case 6 is well identified by using the normalized strain modal shape difference index and the enhanced strain modal shape difference index. Taking the strain response signal of case 6 as an example, it is proved that the stochastic subspace strain modal parameter identification algorithm has strong anti-interference ability under the action of 1.5 times, 4 times, and 9 times noise. In addition, the method is verified by theoretical calculation and numerical simulation, and the damage law has a high degree of coincidence with the test. The experimental results show that this method expands the theoretical basis of foam metal damage degree information identification and improves the accuracy of damage location information identification and the anti-interference of parameter identification. © 2023 Xinyu He et al.
引用
收藏
相关论文
共 1 条
  • [1] Automatic point detection on cephalograms using convolutional neural networks: A two-step method
    Hori, Miki
    Jincho, Makoto
    Hori, Tadasuke
    Sekine, Hironao
    Kato, Akiko
    Miyazawa, Ken
    Kawai, Tatsushi
    Dental Materials Journal, 2024, 43 (05): : 701 - 710