The purpose of this paper is to automate the interpretation of data during ultrasonic diffraction using a non-destructive testing (NDT) technique to accurately size defects for assisting in decision-making. A convolutional neural network (CNN) architecture was developed to automatically measure the length of the defect. Using the architecture, the population of A-scan signals in the scanning path was classified. The defect region was extracted and its size in the scanning direction was obtained by the connected region solution algorithm based on the classification results. The arrival time of diffraction waves was accurately identified by the intelligent denoising framework proposed, combined with Hilbert transform, and then the height of defects was calculated by corresponding geometric relations. The estimation results demonstrate that the measurement method can be considered as a useful technique for crack sizing in industrial structures, even in the case of complex noise.

This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.

A compact five-element integrated ultrasonic transducer was designed and manufactured for the inspection of defects in the split pins of control rod guide tubes in nuclear power plants. The transducer consists of three types of ultrasonic elements to detect cracks in the entire volume of the split pins. In the transducer design, two main factors were investigated: the coupling of elements in confined space and measurement sensitivity. The experimental results demonstrated that the developed transducer has good acoustic performance and defect response capabilities and can detect 10 mm × 2 mm × 0.5 mm notches in the three areas of the split pins. This work provides a foundation for applications in nuclear site inspections.

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced.

The reliable detection of embedded defects is critical for ensuring the structural integrity of metallic components produced by additive manufacturing (AM). However, strong acoustic attenuation in AM materials severely limit the performance of conventional ultrasonic inspection techniques. In this study, a laser–piezo hybrid ultrasonic focusing method based on diffracted shear (DS) waves is proposed to improve defect detectability in metal AM structures. In this approach, a bottom-mounted piezoelectric transducer excites longitudinal ultrasonic waves, while a laser Doppler vibrometer scans the upper surface to acquire transmitted signals. Finite element simsulations reveal that longitudinal waves interacting with internal defects generate DS waves that carry strong feature information related to defect size and position. By leveraging these wave characteristics, a synthetic aperture focusing algorithm was developed to selectively reconstruct DS wave fields and achieve high-resolution imaging of embedded defects. Experiments were conducted on selective laser melting 316L stainless-steel samples containing pre-fabricated through-hole defects. The proposed method achieved clear imaging of subsurface defects with diameters as small as 0.5 mm at depths up to 25 mm. The results demonstrate that focusing on DS waves enables accurate localization of deep-lying defects and provides a potential framework for future in-situ ultrasonic monitoring in metal AM.