The influence of ultrasonic nanocrystal surface modification (UNSM) on the fatigue fracture characteristics of Ti6Al4V was investigated. Two groups of specimens were separated due to different heat treatment conditions. Group one was stress-relief annealed at 650 °C, and group two was then treated with solid solution-aging. UNSM with the conditions of a static load of 25 N, vibration amplitude of 30 μm, and 36,000 strikes per unit produced about 40 μm surface severe plastic deformation (SPD) layers on both groups of specimens. UNSM improved the microhardness and the compressive residual stress. UNSM also helped achieve a neat surface, almost without changing the surface roughness. The fatigue strengths of these two groups were improved by 7% and 11.7%, respectively. After UNSM, fatigue cracks mainly initiated from the surface of the specimen before the fatigue life of 10⁶ cycles, while they appeared at the internal compress deformed α-phase at the zone between the SPD layer and the core after the fatigue life of 106 cycles. The cracks usually extended along the deformation overflow bands and the process traces on the surface. Through the change of micro-dimples in the fatigue final rupture region, nanocrystals were achieved in the SPD layer. The crystal slip and the surface remodeling together influenced the energy field of crack evolution.

To facilitate the manufacturing of the thin-walled components of 2219 aluminum alloy, the quenching–forming–aging (Q–F–A) process has been increasingly utilized. However, natural aging (NA) after quenching significantly affects the cold forming performance of this alloy. In this study, experiments are conducted to investigate the effect of NA time on the cold forming performance of 2219 aluminum alloy. The results indicate that NA can weaken the Portevin–Le Chatelier (PLC) effect, thereby reducing its influence on the cold forming performance of the alloy. The PLC effect becomes indistinct when the aging time reaches 2 years. The yield strength of 2219 aluminum alloy increases monotonically with aging time, while the elongation first increases rapidly and then decreases. After an aging time of 2 years, the yield strength increases by 28.6% from that of newly quenched alloys. The strain hardening index and hardening coefficient indicate that short-term NA (less than 4 days) increases the work hardening rate, while long-term NA reduces it. Microstructural analysis shows that the strengthening effect of NA on 2219 aluminum alloy is mainly due to the growth of G.P. zones and the precipitation of θ″ phases. The NA precipitation behavior can also cause the aggregation of solute atoms and weaken the PLC effect.

A deep learning-based infrared nondestructive testing method is proposed for the internal defects or damages that may be formed in carbon fiber reinforced polymer during long-term service. The method collects infrared thermal radiation data of the specimen with the help of an optically-excited infrared thermal imaging system, combines with image preprocessing technology to construct a semi-supervised learning dataset, and employs a multi-stream-enhanced semi-supervised deep learning algorithm to solve the problems of data scarcity and labeling difficulty. In addition, the Pixel-Boosting framework is introduced to realize the accurate identification and segmentation of defective pixels through background pixel simulation, which significantly improves the detection performance in complex noise environments. Finally, the Pixel-Match algorithm is formed by combining the multi-stream enhancement algorithm and the Pixel-Boosting framework. The F -score value is used as the main evaluation index, and it is compared and analyzed with the fully supervised model (DeepLabV3+) and the semi-supervised model (U ² PL). The experimental results show that the Pixel-Match algorithm achieves an average F -score value of 90.96%, which is 6.72% and 9.2% higher compared to DeepLabV3+ (84.24%) and U ² PL (8ae162c1.76%), respectively, and demonstrates excellent robustness.

In this paper, the inverse problem of identifying the source term of the time fractional diffusion-wave equation is studied. This problem is ill-posed, i.e., the solution (if it exists) does not depend on the measurable data. Under the priori bound condition, the condition stable result and the optimal error bound are all obtained. The fractional Landweber iterative regularization method is used to solve this inverse problem. Based on the priori regularization parameter selection rule and the posteriori regularization parameter selection rule, the error estimation between the regularization solution and the exact solution is obtained. Moreover, the error estimations are all order optimal. At the end, three numerical examples are given to prove the effectiveness and stability of this regularization method.

This study investigates the acoustic emission (AE) response and damage mode characteristics of ±45° glass fiber-reinforced polymer (GFRP) composites used in wind turbine blade shear webs under quasi-static tensile loading. It aims to establish the relationship between AE features and three typical damage mechanisms—matrix cracking, interfacial debonding, and fiber fracture—to support damage assessment and structural health monitoring. Quasi-static uniaxial tensile tests with synchronous AE monitoring are conducted on specimens with three orientations (0°, 45°, and 90°). AE features are selected using correlation analysis and principal component analysis, and the HAC-initialized K-means clustering method is employed for damage mode identification. The optimal number of clusters is determined to be three, according to the Davies–Bouldin index (DBI) and the Silhouette index (SI). The resulting low-, mid-, and high-frequency clusters are associated with matrix cracking, interfacial debonding, and fiber fracture, respectively. These interpretations are further supported by wavelet-based time–frequency analysis and microscopic fracture surface observations.