A novel method is proposed in this paper for simultaneously locating and quantifying damage in composite plates by employing Lamb waves and the algorithm of convolution neural network. The interaction between Lamb wave and damage of different degrees is also studied by simulation. The experiments on Lamb wave are carried out by employing a square array which is composed of 4 piezoelectric wafers. First of all, the sensor array collects response signals of Lamb wave as training data, and then de-noises them adopting the method of wavelet transform. In the process, the damage caused to composite can be realized through mass blocks. Besides, the Fourier transform is applied for the extraction of the characteristics shown by the signals. After that, the spectrum with the characteristics of damage and corresponding damage modes are employed as input and output of convolutional neural network respectively, and accordingly the model of damage identification is established. Finally, 191 samples (from a total of 192) were identified accurately and the correct recognition rate achieved is 99.5%, which consequently demonstrates that convolution neural network can be employed to establish the complex mapping relationship between signal and damage, and further proves that the proposed method performs well in high accuracy and great potential in simultaneous localization and quantitative identification of damage existing in composite plate.

This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector’s prior-knowledge, which can lead to advantages in automatic ultrasonic testing.

The in-situ observation is of great significance to the study of the growth mechanism and controllability of two-dimensional transition metal dichalcogenides (TMDCs). Here, the differential reflectance spectroscopy (DRS) was performed to monitor the growth of molybdenum disulfide (MoS₂) on a SiO₂/Si substrate prepared by chemical vapor deposition (CVD). A home-built in-situ DRS setup was applied to monitor the growth of MoS₂ in-situ. The formation and evolution of monolayer MoS₂ are revealed by differential reflectance (DR) spectra. The morphology, vibration mode, absorption characteristics and thickness of monolayer MoS₂ have been confirmed by optical microscopy, Raman spectroscopy, ex-situ DR spectra, and atomic force microscopy (AFM) respectively. The results demonstrated that DRS was a powerful tool for in-situ observations and has great potential for growth mechanism and controllability of TMDCs prepared by CVD. To the best of the authors’ knowledge, it was the first report in which the CVD growth of two-dimensional TMDCs has been investigated in-situ by reflectance spectroscopy.