Ti matrix composites reinforced with 1–5 wt% graphene oxide (GO) were prepared by hot-pressed sintering in argon atmosphere. The effect of sintering temperature on the microstructures and mechanical properties of the composite was also evaluated. The results show that TiC nanoparticles were formed in situ as interfacial products via the reaction between Ti and GO during sintering. With increases in GO content and sintering temperature, the amount of TiC increased, improving the mechanical properties of the composites. GO was also partly retained with a lamellar structure after sintering. The composite reinforced with 5 wt% GO exhibited a hardness of 457 HV, 48.4% higher than that of pure Ti at 1473 K. The Ti-2.5 wt% GO composite sintered at 1473 K achieved a maximum yield stress of 1294 MPa, which was 62.7 % higher than that of pure Ti. Further increasing the GO content to 5 wt% led to a slight decrease in yield stress owing to GO agglomeration. The fracture morphology of the composite reinforced with GO exhibited a quasi-cleavage fracture, whereas that of the pure Ti matrix showed a ductile fracture. The main strengthening mechanism included grain refinement, solution strengthening, and dispersion strengthening of TiC and GO.

The effects of stacking faults (SFs) on the mechanical properties of high-entropy alloys (HEAs) may not be ignored because of the lower or even negative SF energy. Here, we investigated the SF strengthening of CoCrFeMnNi and clarified its potential mechanism. It showed experimentally that the CoCrFeMnNi with more SFs had higher strength and hardness than that with fewer SFs, revealing significant SF strengthening. To gain an insight into such effects, we performed simulations for the mechanical responses of CoCrFeMnNi samples with different plane defects at the atomic scale. The results showed that the strengths of the CoCrFeMnNi samples containing SFs or twin boundaries were higher than that of single crystal ones. Overall, our results suggested that the effects of SFs on the mechanical properties of FCC HEAs could not be ignored, and the atomistic insights into the strengthening mechanism of HEAs we provided would be beneficial to improve the mechanical properties of FCC HEAs.

In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time–temperature parametric models, machine learning models, and a new method combining time–temperature parametric models with machine learning models are used to predict the creep rupture life of a small-sample material. The prediction accuracy of each model is quantitatively compared using model evaluation indicators (RMSE, MAPE, R²), and the output values of the most accurate model are used as the output values of the prediction method. The prediction method not only improves the applicability and accuracy of creep rupture life predictions but also quantifies the influence of each input variable on creep rupture life through the machine learning model. A new method is proposed in order to effectively take advantage of both advanced machine learning models and classical time–temperature parametric models. Parametric equations of creep rupture life, stress, and temperature are obtained using different time–temperature parametric models; then, creep rupture life data, obtained via equations under other temperature and stress conditions, are used to expand the training set data of different machine learning models. By expanding the data of different intervals, the problem of the low accuracy of the machine learning model for the small-sample material is solved.

In this study, a numerical approach—the discontinuous Meshless Local Petrov-Galerkin-Eshelby Method (MLPGEM)—was adopted to simulate and measure material plasticity in an Al 7075-T651 plate. The plate was modeled in two dimensions by assemblies of small particles that interact with each other through bonding stiffness. The material plasticity of the model loaded to produce different levels of strain is evaluated with the Lamb waves of S₀ mode. A tone burst at the center frequency of 200 kHz was used as excitation. Second-order nonlinear wave was extracted from the spectrogram of a signal receiving point. Tensile-driven plastic deformation and cumulative second harmonic generation of S₀ mode were observed in the simulation. Simulated measurement of the acoustic nonlinearity increased monotonically with the level of tensile-driven plastic strain captured by MLPGEM, whereas achieving this state by other numerical methods is comparatively more difficult. This result indicates that the second harmonics of S₀ mode can be employed to monitor and evaluate the material or structural early-stage damage induced by plasticity.

Fe-Cr-Nb-C wear-resistant alloy coatings were successfully fabricated on high-carbon forged steel via coaxial powder feeding laser cladding. The evolution of microstructure and wear resistance with varying Nb content was systematically investigated. The results indicate that appropriate NbC addition markedly modifies the distribution of grain and boundary carbides. As Nb content increases from 2.5 wt% to 3.5 wt%, nanoscale rod-like NbC precipitates form uniformly along boundaries, effectively suppressing the formation of brittle Cr₂₃C₆ precipitation. Semi-coherent NbC/matrix interfaces and NbC-induced grain refinement reduce adhesive/abrasive wear, thereby improving hardness and wear resistance. At 4.5 wt% Nb, discrete micron-sized NbC particles form within the grains, yielding optimal performance. However, excessive Nb (≥5.5 wt%) causes NbC agglomeration, inducing stress concentrations and large spallation pits that deteriorate wear resistance. This work highlights NbC morphology as a key factor for tailoring coating properties.