Centrifugally cast 35Cr45NiNb alloy has been widely employed in ethylene-cracking furnace tubes owing to its excellent carburization and creep resistance. However, the influence of microstructural degradation and temperature on its high-temperature tensile behavior remains poorly investigated. In this study, an accelerated aging method at 1200 °C for 230 h (A1) and 430 h (A2) was employed to simulate approximately 4 and 8 years of service at 1050 °C, based on the Larson-Miller parameter. The equivalence was validated by the nearly identical precipitate area fractions of the A1 specimen (16.6%) and an ex-service specimen (14.8%). Combined with SEM and EBSD characterization, tensile tests at 950, 1000, and 1050 °C were conducted to elucidate the relationship between microstructure and high-temperature tensile properties. During aging, the skeletal interdendritic M7C3 carbides transformed into blocky M23C6, NbC evolved into the brittle G-phase (Ni16Nb6Si7), fine secondary M23C6 precipitates formed, and the initially continuous primary-carbide network progressively coarsened. Yield and ultimate tensile strengths decreased monotonically with increasing temperature, whereas aging produced pronounced hardening at the expense of ductility, as secondary-carbide precipitation strengthening outweighed the weakening of the primary carbide network. The fracture mode transitioned from mixed quasi-cleavage fracture at 950 °C, initiated by stress concentration at coarse phase interfaces, to ductile rupture at 1000 and 1050 °C. GND analysis further revealed an aging-dependent transition in the dominant deformation mechanism, from dislocation pile-up at the carbide network, to recrystallization after prolonged aging.

This study investigates the atomic-scale effects of hydrogen concentration and Ni content on crack propagation in Fe-Ni alloy models using molecular dynamics methods. A Mode I crack model with a (001)[100] orientation was constructed, and hydrogen atoms were locally introduced at the crack tip with concentrations of 5.3 at.% and 14.3 at.%. Fe-Ni alloy models with 5%, 10%, 15%, and 20% Ni were compared in terms of crack growth, dislocation evolution, stacking fault energy, and hydrogen diffusion. The results show that local hydrogen introduction has a limited effect on the peak stress-strain response, while hydrogen clearly accelerates crack propagation in the middle stage, especially at high concentrations. For the 10% Ni model, the middle-stage crack growth rate increases to 0.36 Å/ps under 14.3 at.% crack-tip hydrogen. Crack growth in all models shows three stages. The 15% Ni model exhibits a clear plateau in the second stage and the shortest final crack length. Further analysis shows that Ni content regulates dislocation behavior through stacking fault energy. At 15% Ni, sustained dislocation entanglement and high-density dislocation multiplication occur near the crack tip, which helps dissipate local stress. Hydrogen diffusion analysis indicates that hydrogen mobility is lower in the 15% Ni model, which may be related to hydrogen retention near dislocation-rich regions. A normalized comparison based on hydrogen diffusion and middle-stage crack growth rate further identifies 15% Ni as the lowest crack propagation tendency composition among the studied models. These results provide atomic-scale data for Ni-content optimization in hydrogen-resistant alloys, although the direct engineering transfer of the findings is limited by the length and time scales of molecular dynamics simulations.

The propagation behavior of fatigue short crack (SC) in metallic materials was examined in this review, with a focus on recent advances in experimental methods and modeling approaches. SC posed unique challenges, particularly in marine and welded structures, where traditional linear elastic fracture mechanics (LEFM) fell short due to their inability to capture the microstructural sensitivity and fluctuating growth characteristics of SC. Various experimental methods, including the replica method and in-situ SEM observation, were evaluated, and their effectiveness and limitations in SC characterization were highlighted. Additionally, advanced modeling approaches were discussed to address SC complexities, especially in heterogeneous materials like welded joints. The need for a unified model that integrated microstructural effects to enhance fatigue life predictions was emphasized, aiming for practical engineering applications in complex environments.

This paper presented a comprehensive investigation of the tensile properties and fatigue crack growth behavior of EH36 steel joints. Tensile deformation and fracture mechanisms were elucidated using digital image correlation (DIC). For the fatigue property, DIC was developed to predict the growth rate (da/dN), providing insights into crack propagation behavior under dynamic loading conditions. Additionally, a novel approach for predicting fatigue damage (D) based on the maximum strain (epsilon max) characterized by DIC was established, enabling the assessment of fatigue damage states in EH36 structural components across various stress ratios (R). However, DIC showed limitations in addressing initial short crack propagation description, which suggested the need for alternative methods to effectively tackle this challenge.

This paper investigated the effect of axial misalignment on the fatigue strength and fatigue crack propagation rate of butt weld joints made of SUS301L austenitic stainless steel thin sheets. The impact of axial misalignment on the fatigue performance of thin sheet welded joints was revealed through microstructure observation, tensile test, fatigue performance test and fatigue crack propagation rate test. The results indicated that the microstructure and tensile properties of both joints were similar. However, the misaligned joint exhibited a higher stress concentration, which caused the fatigue strength of the S–N curve to decrease significantly. After analyzing the fatigue data for misaligned welded joints with the IIW recommended stress amplification factor, the study found the amplified results were lower compared to butt joints. This suggested that the impact of axial misalignment on the fatigue properties of thin sheet welded structures was more significant than on thick plate welded structures. Furthermore, there was a considerable increase in the stress intensity factor at the fatigue crack tip of the misaligned joint, leading to a substantial rise in the propagation rate of the fatigue crack.

This work systematically investigated the d a /d N of different micro-zones of S355/S420 welded joints in air and corrosion environment. The results indicated that the ability to resist crack propagation of base metal was better than that of welded metal. In addition, the d a /d N at low stress intensity factor specimens in corrosion was slightly lower than that of specimens in air, which was revealed by the finite element method. The corresponding results indicated that the corrosion products were deposited on the crack surface, which would induce the stress close to the metallic yield stress at the crack tip, resulting in the low d a /d N under corrosion.

The prediction of the combined high and low cycle fatigue (CCF) life of welded components was valuable for the reliability of practical engineering structures. In this study, a physics-informed Grey-deep convolutional neural networks (DCNN) model was constructed and the model parameters was determined based on the fatigue behavior characteristics of CCF, which was proved perform well in the CCF life prediction of welded components. The predicted results indicated that the proposed model can realize reliable CCF life prediction with good accuracy (MAE = 0.49) and stability (RMSE = 0.44). Further, the proposed model also exhibited good prediction performance under different loading and geometry conditions, indicating the good universality of the proposed model. And the good universality represented that the proposed model had the potential to be applied in more engineering fields.

The fatigue life extension approaches played an important role in ensuring the safety of marine engineering structures. This study conducted an in-depth analysis of the S-N curves of welded structures under different service environments and different fatigue life extension approaches, and found that the comprehensive life extension process of welding toe polishing and coating (the fatigue life was extended by 15-33 times compared to untreated samples) was the most significant approach under dry air medium environment. The comprehensive life extension process of welding toe polishing and coating (the fatigue life was extended by 10-25 times compared to untreated samples) was the most significant approach under salt spray corrosive medium environment. Moreover, the S-N curve and related parameters of welded joints of semi-submersible platform under different environmental media conditions and different combinations of fatigue life extension approaches were studied in depth, which had important guiding significance for practical life extension tools of jacket platform T-joints in practice.

Investigation on fatigue life prediction approaches for welded joints of different materials was of great significance for the reliability and safety of engineering structures. A fatigue performance dataset with sufficient characteristics was established via data processing and augmentation. The modification of loss function and the physics-informed influencing factors was used to realize the multi-level physical intervention on the prediction model. The importance of physics-informed influencing factors under mechanical-properties dataset was analyzed and integrated into the deep convolutional neural network (DCNN) framework via attention model. The significance of the intervention using physics-informed influencing factors and the identification using mechanical properties was proved via the comparison of different prediction methods. Further, a mechanical properties-based prediction method for fatigue life of multiple materials was established, and the average prediction error (30.5%) and standard deviation of prediction error (16.1%) of the proposed approach were significantly lower than that of the other prediction methods. By the identification via mechanical properties and the introduction of physics-informed influencing factors, the machine learning method can be used to evaluate the fatigue life of multiple materials in engineering with low consumption.

This work systematically revealed the static-dynamic corrosion failure behavior of EH36 joints under air, simulated seawater and cathodic protection in seawater. The result indicated that the seawater environment affected tensile performance slightly but acted a critical role in the fatigue behavior. Specifically, the corrosion pit and cyclic stress would respectively induce high-stress concentration and corrosion current density, significantly decreasing the fatigue strength of joints. The application of cathodic protection on joints in seawater would decrease the tensile behavior, but would continuously provide electrons for the surface and crack tip to prevent corrosion damage, improving fatigue performance of EH36 joints.

A generalization ability-enhanced approach for corrosion fatigue life prediction was proposed. The data augmentation and analysis of influence factors were conducted via Borderline-SMOTE and XGBoost algorithms to improve the prediction accuracy. The results of weight analysis were integrated into DCNN model via Attention mechanism. The average error and error standard deviation of the proposed model were more than 89.8% and 87% smaller than that of exiting models, respectively. The average gradient-signal-to-noise-ratio was introduced to evaluate the generalization ability of the proposed model, which was significantly higher than that of the linear regression. The predicted results under different stress ratios, frequencies and environmental conditions emphasized the good prediction accuracy and generalization ability of the proposed model. Consequently, the proposed method could reduce the consumption of fatigue test and evaluate the reliability of engineering components, which could provide a technique support for the intellectualization and digitization of fatigue behavior of offshore platforms welded joints.

This work investigated the crack growth behaviors of EH36 joints, 316H steels, 6063-T6 and TC4 alloys at room temperature, proposing a unified approach. This model was achieved via the product of monotonic tensile crack tip displacement (phi mt) and cyclic crack tip displacement (phi c), in which phi mt also considered the metallic capability to affect crack propagation behavior to eliminate the effect of material factor on crack growth rate (da/dN). Cyclic plastic zone size (rcp) in phi c could describe short crack process and K in phi mt could be used for the pre-sentation of long crack behavior. Based on this method, metallic da/dN were linearly related to phi mt phi c under several variables, such as stress, microstructures, crack scale and loading ratio.

The fatigue life prediction of welded joints with different specifications under different conditions was a challenging issue due to the quite complex influence. Specifically, the current fatigue life prediction methods lacked comprehensive analysis of multiple influence factors and reasonable incorporation of physical models. So, the analysis of factors influencing the fatigue life of welded joints and the fatigue life prediction were critical to the safety and reliability of engineering structures. In this study, a prediction approach for fatigue performance was proposed based on influence factor analysis using data-driven methods. The fatigue performance dataset was processed via physical models to realize the physics-based analysis of the factors affecting fatigue performance. The weights of the physics-informed influence factors on the fatigue life were analyzed using the extreme gradient boosting (XGBoost) algorithm and verified by cross-validation. The prediction results extracted from the SN curves predicted by the deep convolutional neural network (DCNN) model incorporating the weight analysis of influence factors exhibited better accuracy and stability than direct prediction and other existing prediction models. Because of the advantages of DCNN in avoiding over fitting and local optimization, the proposed approach can better describe and estimate the fatigue performance of welded structures.

In-situ test was conducted to investigate the effect of overload (OL) on fatigue short crack growth rate (FSCGR). Mechanisms of dual crack growth competitions and overload-induced acceleration/retardation were revealed using digital image correlation (DIC) technology. Eventually, the plastic zone sizes obtained by DIC and Irwin model were compared to propose an OL-induced model to describe FSCGR.

The ceramics reinforced medium entropy alloy base composites (MEABCs) were manufactured by ultrasonic vibration (UV) assisted laser cladding (LC) process, which were investigated by scanned electron microscope (SEM), electron backscattered diffraction (EBSD) and transmission electron microscope (TEM). The microstructure of MEABCs was mainly composed of body centered cubic (BCC), intermetallics and TiN ceramics, the fine dendrites were produced under the action of the cavitation and the acoustic flow effects of UV, increasing the dislocation density to promote the low angle grain boundary (LAGBs). The micro-hardness and the wear resistance of the composites were enhanced by the second phase and fine grain strengthening. The diffusion of the elements such as Cr/Ni and the generation of LAGBs contributed to the enhancement of the corrosion resistance of the composites.

For improving the comprehensive mechanical properties of aluminum alloys (AAs) joint, the 6063-T6 AAs with thickness of 10 mm were bonded via the double-sided (DS) friction stir welding technique. Compared with the single-sided (SS) AAs joint, the grains in various regions of DS joints were refined in various degrees. In addition, the high-angle grain boundaries frequency of nugget zone reached to approximately 55%, which could hinder crack growth. Fatigue test result indicated that fracture positions of DS joints were either at thermo-mechanically affected zone or heat-affected zone near advancing side, resulting in the fatigue strength of 92 MPa. However, the fatigue strength of SS joints those mainly fractured on welding root was 76 MPa. The approximately 20% higher fatigue strength of DS joint than that of SS joint was mainly attributed to the elimination of the weak bonding at welding root as well as the grain refinement.

This paper proposes a prediction model of welded joint fatigue properties based on single -parameter decision-theoretic rough set (SPDTRS)-cuckoo search (CS)-artificial neural network (ANN) hybrid algorithm. To establish the fatigue performance database of EH36 steel, the pre-processing and data cleaning are carried out by analytic hierarchy process (AHP) and box-plot method to obtain reliable fatigue properties prediction. Therein, the SPDTRS theory is used to analyze the weight of fatigue properties influencing factors, the CS algorithm is used to avoid the over-fitting and local optimization of ANN. During process, the influencing factors are regarded as input and the material related parameters C and m are conducted as output to realize the fatigue properties prediction, which improves the accuracy and stability of the present prediction method. According to the comparisons between the experimental and predicted results, it is found that the predicted S-N curves are within +/- 1.1 error band of the experimental results, the average error of fatigue life is less than 10%, and can be within +/- 1.2 error band. As a result, the fatigue properties prediction model reasonably shows the fatigue properties of welded structures, and provides a certain reference for fatigue design of welded structures.

For better revealing the behavior and mechanism of fatigue short crack (SC) propagation, five different kinds of notched samples were designed. Based on the crack growth rate-SC length map, finite element method and electron backscatter diffraction (EBSD) pattern, the evolution theory of geometrically necessary dislocation density (rho(GND)) versus SC length was proposed to distinguish the physically short crack (PSC) length; Schmid factor was utilized to define the length of microstructurally short crack (MSC). Thus, the whole SC growth was divided into three parts: MSC, transition zone and PSC. Finally, an original piecewise model containing two parameters was proposed to describe the whole SC growth of EH36 steels.

Aiming at short crack (SC) behavior whose growth rate (da/dN) decreased firstly and subsequently increased with the rise of SC in EH36 steel, the crack transition length (a(t)) concept was introduced to divide SC into 3 stages: microstructurally short crack (0-a(t)), transition zone (a(t)-0.5 mm) and physically short crack (0.5-1.5 mm). Then geometrically necessary dislocation density was utilized to reveal strain hardening gradient effect on SC behavior. Eventually, considering crack closure, the effective stress intensity factor range (delta K-eff) and the delta K-eff when SC was equal to at (delta K-at) were obtained to form a segmented model to demonstrate da/dN of whole SC.

SUS301L stainless steel joints with thickness of 2 mm were manufactured via the tungsten inert gas (TIG) and metal active gas arc (MAG) techniques, respectively. Electron backscatter diffraction results indicated that δ-ferrites with volume fraction of approximately 1.8% were formed in the weld seam of TIG joints, which was unbeneficial for the mechanical strength. By contrast, the microstructures were relatively fine and no δ-ferrites were formed in the MAG joints, showing a better microstructural quality compared with that of TIG joints. Nevertheless, the fatigue test results indicated that fatigue strength of TIG joints was higher compared with that of MAG joints. This was because that the lower weld reinforcement of TIG joints decreased the stress concentration at weld toe than that of MAG joints, which resulted in the better fatigue performance, implying that the fatigue behavior of welded joints was more influenced by the welding quality instead of microstructures.