Abstract
Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.
Highlights
Carbon fiber reinforced plastics (CFRPs) are widely used in aerospace, military, marine, and automotive industry due to their high specific strength and stiffness [1]
An impact damage in a CFRP consists of various failure mechanisms such as matrix cracking, An impact damage in a CFRP consists of various failure mechanisms suchfailure as matrix cracking, to delamination, and fiber breakages, etc
poly(vinylidene fluoride) (PVDF) signals induced by impact damages in CFRP specimens were measured, and the determination of damages was performed based on discrete wavelet transform (DWT) and a Support vector machine (SVM) algorithm
Summary
Carbon fiber reinforced plastics (CFRPs) are widely used in aerospace, military, marine, and automotive industry due to their high specific strength and stiffness [1]. Much less works have been done to analyze the relationships between PVDF signals and impact damage process and mechanisms This is because in-depth analysis of PVDF signals takes a great effort due to the low sensitivity of PVDF and complexity of the impact damage signals. Another approach to signal analysis has been to use classification methods based on machine learning algorithms. An impact damage determination model based on SVM was constructed by training the model with PVDF signal features according to the damage state of the impacted specimens. The damage states, which was difficult to be analyzed due to the complexity of signals, could be effectively determined by combining spectral analysis and the SVM algorithm
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