The effect of hydrogen content on the microstructure, mechanical properties, and fracture mechanisms of low-carbon lath martensitic steel was investigated using both experimental methods and atomistic modeling. Tensile testing revealed a transition in the fracture behavior with increases in hydrogen concentration. Specifically, at a hydrogen content of 0.44 wppm, a shift from transgranular to intergranular fractures was observed. The most probable cause of hydrogen embrittlement was identified to be HELP-mediated HEDE. As the hydrogen concentration increased, the dislocation density in close-packed planes, such as (111) and (100), was found to rise. The key differences between the hydrogen-free and hydrogen-charged specimens were the localization and density of dislocations, as well as the change in the distribution of slip bands. Atomistic modeling supported these experimental findings, showing that “quasi-cleavage” cracks predominantly initiate at block boundaries with higher local hydrogen accumulation. These results underscore the significant role of hydrogen in modifying both the microstructural characteristics and fracture behavior of low-carbon martensitic steel, with important implications for its performance in hydrogen-rich environments.

This study investigates the fabrication, wear characterization, and coefficient of friction (COF) prediction of open-cell AlSn6Cu–Al₂O₃ composites obtained by a liquid-state processing technique. Focusing on wear behavior under varying loads using the pin-on-disk method, this research characterizes microstructure and phase composition via SEM, EDS, and XRD analyses. A novel aspect of this research is the application of an LSTM recurrent neural network model for the fast and accurate prediction of the COF of the composites, eliminating the need for extensive experimental work. Additionally, feature importance analysis using Random Forest regressors is conducted to ascertain the relative contribution of each input variable to the output variable, enhancing our understanding of the wear mechanisms in these materials. The results demonstrate the effectiveness of the composite’s reinforcement in improving wear resistance, highlighting the critical role of mechanical stress and the reinforcement’s hardness in the wear process. The quantitative findings related to the wear behavior include a mass-wear reduction in the open-cell AlSn6Cu–Al₂O₃ composite from 8.05 mg to 1.90 mg at 50 N and a decrease from 17.55 mg to 8.10 mg at 100 N, demonstrating the Al₂O₃ particles’ effectiveness in improving wear resistance under different loads.

This study investigates the effect of hydrogen charging time on the mechanical properties and microstructural evolution of low-carbon ferrite–pearlite steel that has been in service for over 30 years in natural gas transmission. Specimens were subjected to in-situ electrochemical hydrogen charging for varying durations, followed by tensile testing. Detailed microstructural analysis was performed using scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Despite negligible changes in the overall hydrogen content ($C_H$≈ 4.0 wppm), significant alterations in fracture morphology were observed. Fractographic and TEM analyses revealed a clear transition from ductile fracture in uncharged specimens to a predominance of brittle fracture modes (quasi-cleavage, intergranular, and transgranular) in hydrogen-charged samples. The results show time-dependent microstructural changes, including increased dislocation density and the formation of prismatic loop debris, particularly within the ferrite phase. Prolonged charging leads to localized embrittlement, which is explained by enhanced hydrogen trapping at ferrite-cementite boundaries, grain boundaries, and dislocation cores. TEM investigations further indicated a sequential activation of hydrogen embrittlement mechanisms: initially, Hydrogen-Enhanced Localized Plasticity (HELP) dominates within ferrite grains, followed by Hydrogen-Enhanced Decohesion (HEDE), particularly at ferrite-cementite interfaces in pearlite colonies. These findings demonstrate that extended hydrogen charging promotes defect localization, dislocation pinning, and interface decohesion, ultimately accelerating fracture propagation. The study provides valuable insight into the degradation mechanisms of ferrite-pearlite steels exposed to hydrogen, highlighting the importance of charging time. The results are essential for assessing the reliability of legacy pipeline steels and guiding their safe use in future hydrogen transport infrastructure.

In this paper, we investigate how two different microstructures in pure iron affect the dislocation mobility in hydrogen-charged and non-charged samples by conducting stress-relaxation tests. The effective activation volume of the pure iron for both types of microstructures (cold-rolled and annealed samples) has been determined for both H-charged and uncharged material. Information about the dislocation structures formed during stress relaxation is provided by conducting TEM analysis. We employ a self-consistent kinetic Monte-Carlo (SCkMC) model of the ½ [111] screw dislocation in Fe to investigate how hydrogen affects the mobility and behavior of the dominant mobile dislocation in Fe at different stresses and H concentrations. The results from our simulations show the following: (i) at low stresses the deviation from the primary slip plane in the presence of H is lower than the deviation in the uncharged Fe. The deviation angle decreases with increasing H concentration; (ii) at higher shear stresses, the higher probability for kink-pair formation in the secondary (110) planes in the presence of H, leads to an enhanced deviation from the primary slip plane, which increases with increasing H concentration. We use the results of stress-relaxation tests and SCkMC simulations to propose an explanation for the formation of dislocation cell structures in pure and hydrogen charged Fe in the cold-rolled and annealed samples.

Aluminum-based metal matrix composites reinforced with B₄C are advanced materials recognized for their exceptional combination of lightweight properties, high hardness, and superior wear resistance. These characteristics make them perfectly suited for applications demanding exceptional performance in extreme mechanical and tribological environments. This study investigates the wear behavior, microstructural characteristics, and predictive modeling of Al10Cu-B₄C composites fabricated via powder metallurgy with varying B₄C contents (0, 2.5, 5, and 7.5 wt.%). The addition of B₄C microparticles to Al10Cu composites significantly influenced their tribological properties with 2.5 wt.% B₄C achieving a 21.74% reduction in the coefficient of friction (COF) and 7.5 wt.% B₄C providing a remarkable 65.00% improvement in wear resistance. Microstructural analysis using SEM and EDS was conducted on the unreinforced materials and the reinforced composites both before and after the wear tests. To further analyze and predict the tribological performance, a Gated Recurrent Unit neural network was developed to predict COF values. The need for this model arises from its potential to cost-effectively facilitate the prediction of COF, diminishing the need for extensive experimental testing while being noted for its simplicity and ease of implementation in practical applications. The model achieved excellent accuracy with an R² of 0.9965 for the test set and 0.9917 for the validation set. Additionally, feature importance analysis using Random Forest models identified reinforcement-related features as the dominant predictors for both COF and mass wear. These findings demonstrate the potential of Al10Cu-B₄C composites for emerging industrial applications, where enhanced wear resistance and controlled friction are critical for improving efficiency and durability under rigorous operating conditions. Furthermore, this study highlights the efficacy of neural network models in accurately predicting COF, providing a powerful tool for optimizing the performance of advanced composite materials.

Cr-based composite coatings with superior wear resistance are in growing demand for high-performance applications in the automotive, aerospace, and general manufacturing sectors. In this study, an Al10Cu alloy produced via powder metallurgy was coated with a chromium/nanodiamond (Cr/ND) composite layer using an electrodeposition process to enhance its tribological performance. The coatings were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The resulting Cr/ND layer exhibited a uniform thickness of 73.5–76.2 μm and markedly improved surface hardness (809.4 HV), representing a 15-fold increase over the uncoated alloy (53.6 HV). Pin-on-disk tribological testing under dry sliding conditions showed complete elimination of detectable mass loss (0.00 mg vs. 0.55 mg for uncoated) within the measurement system resolution, indicating excellent resistance to both abrasive and adhesive wear. XRD analysis revealed the formation of a hexagonal close-packed Cr₂H phase with incorporated nanodiamond particles. To capture and predict the temporal evolution of the friction coefficient, a customized dual-layer long short-term memory neural network—optimized with a look-back window of 3 timesteps and ReLU-activated dense layers—was implemented. The model achieved superior predictive performance on the coated system, with validation and test R² values of 0.9973 and 0.9965, respectively, demonstrating enhanced modeling accuracy for surface-engineered materials. These findings demonstrate a significant advancement in wear protection for aluminum alloys and introduce a robust data-driven approach for real-time friction prediction in engineered surfaces.