Jiang Peng, Li Hui, Yan Xiaowei, Zhang Luying, Li Wei
College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Shandong Gaint E-Tech Co., Ltd., Jinan 250000, China.
Polymers (Basel). 2023 Jun 11;15(12):2647. doi: 10.3390/polym15122647.
This research utilized the sooty tern optimization algorithm-variational mode decomposition (STOA-VMD) optimization algorithm to extract the acoustic emission (AE) signal associated with damage in fiber-reinforced composite materials. The effectiveness of this optimization algorithm was validated through a tensile experiment on glass fiber/epoxy NOL-ring specimens. To solve the problems of a high degree of aliasing, high randomness, and a poor robustness of AE data of NOL-ring tensile damage, the signal reconstruction method of optimized variational mode decomposition (VMD) was first used to reconstruct the damage signal and the parameters of VMD were optimized by the sooty tern optimization algorithm. The optimal decomposition mode number K and penalty coefficient α were introduced to improve the accuracy of adaptive decomposition. Second, a typical single damage signal feature was selected to construct the damage signal feature sample set and a recognition algorithm was used to extract the feature of the AE signal of the glass fiber/epoxy NOL-ring breaking experiment to evaluate the effectiveness of the damage mechanism recognition. The results showed that the recognition rates of the algorithm in matrix cracking, fiber fracture, and delamination damage were 94.59%, 94.26%, and 96.45%, respectively. The damage process of the NOL-ring was characterized and the findings indicated that it was highly efficient in the feature extraction and recognition of polymer composite damage signals.
本研究利用乌黑燕鸥优化算法-变分模态分解（STOA-VMD）优化算法提取与纤维增强复合材料损伤相关的声发射（AE）信号。通过对玻璃纤维/环氧NOL环试样进行拉伸试验，验证了该优化算法的有效性。为解决NOL环拉伸损伤声发射数据混叠程度高、随机性大、鲁棒性差的问题，首先采用优化变分模态分解（VMD）的信号重构方法对损伤信号进行重构，并通过乌黑燕鸥优化算法对VMD参数进行优化。引入最优分解模态数K和惩罚系数α以提高自适应分解的精度。其次，选取典型的单损伤信号特征构建损伤信号特征样本集，并采用识别算法提取玻璃纤维/环氧NOL环断裂试验声发射信号的特征，以评估损伤机制识别的有效性。结果表明，该算法在基体开裂、纤维断裂和分层损伤中的识别率分别为94.59%、94.26%和96.45%。对NOL环的损伤过程进行了表征，结果表明该算法在聚合物复合材料损伤信号的特征提取和识别方面具有很高的效率。
