In this work, the microstructural evolutions and mechanical properties of an as-cast Mg-10Gd-4Y-1.5Zn-0.5Zr (wt %) alloy during successive multi-pass equal channel angular pressing (ECAP) were systematically investigated by X-ray diffractometer, scanning electron microscopy, transmission electron microscopy, and compression test. The obtained results show that the microstructure of as-cast alloy consists of α-Mg grains, Mg₃Gd island phase, few Y-rich particles, and lamellar 14H LPSO (long period stacking ordered) phase located at the grain boundaries. During ECAP, the Mg₃Gd-type phase is crushed and refined gradually. However, the refined Mg₃Gd particles are not distributed uniformly in the matrix, but still aggregated at the interdendritic area. The 14H phase becomes kinked during the early passes of ECAP and then broken at the kinking bands with more severe deformation. Dynamic recrystallization of α-Mg is activated during ECAP, and their average diameter decreases to around 1 μm, which is stabilized in spite of increasing ECAP passes. Moreover, nano-scale γ′ phases were dynamically precipitated in 16p ECAP alloy. Compression tests indicate that 16p ECAP alloy exhibits excellent mechanical property with compressive strength of 548 MPa and fracture strain of 19.1%. The significant improvement for both strength and ductility of deformed alloy could be ascribed to dynamic recrystallization (DRX) grains, refined Mg₃Gd-type and 14H particles, and dynamically precipitated γ′ plates.

Equal-channel angular pressing (ECAP) was performed on a Mg (6 wt %) Zn alloy at temperatures from 160 to 240 °C and the microstructures and mechanical properties were studied using optical microscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and an electronic universal testing machine. The results showed that ECAP was effective for grain refinement and a bi-modal grain structure formed at low temperatures, which was stable during ECAP from 160 to 200 °C. MgZn₂ phase and Mg₄Zn₇ phase were generated during the ECAP process. The mechanical properties remarkably increased after two repetitions of ECAP. However, the strengths could not be further improved by increasing the plastic deformation, but decreased when ECAP was performed between 200 and 240 °C. The mechanical properties of the ECAP Mg-6Zn alloy was determined by a combination of grain refinement strengthening, precipitation hardening, and texture softening.

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.