Fatigue damage of butt‐welded joints is investigated by a damage mechanics method. First, the weld‐induced residual stresses are determined by using a sequentially coupled thermo‐mechanical finite element analysis. The plastic damage of material is then calculated with the use of Lemaitre's plastic damage model. Second, during the subsequent fatigue damage analysis, the residual stresses are superimposed on the fatigue loading, and the weld‐induced plastic damage is considered as the initial damage via an elasto‐plastic fatigue damage model. Finally, the fatigue damage evolution, the relaxation of residual stress, and the fatigue lives of the joints are evaluated using a numerical implementation. The predicted results agree well with the experimental data.
The numerical analysis of low cycle fatigue of HTS‐A steel welded joints under combined bending and local compressive loads are implemented using the damage mechanics approach. First, a finite element numerical simulation of the welding process is employed to extract the welding residual stresses, which are then imported as initial stresses in the subsequent fatigue analysis. Second, a multiaxial fatigue damage model including damage coupled elasto‐plastic constitutive equations and plastic damage evolution formulation is applied to evaluate the mechanical degradation of the material under biaxial fatigue loads. Further, the fatigue lives of the HTS‐A steel welded joints are computed and compared with the experimental results from literature. A series of predicted load‐life curves clearly illustrates the variation of fatigue lives along with the combined loadings. Finally, the effects of local compression on accumulated plastic strain and fatigue damage are studied in detail. It is revealed that the local compression induces a damage competition between two critical zones.
To achieve welding automation, the center of the groove needs to be detected accurately during welding. We developed a method based on template-matching to detect the groove center during gas metal arc welding (GMAW). To avoid the negative influence of the strong GMAW arc light, a high-dynamic-range camera was used to capture details of the welding arc, molten pool, and the V-groove simultaneously in a single image. Two imageprocessing and object-detection algorithms were developed to detect the center of the welding pool and the groove based on template matching. The experimental results of the latter algorithm were more accurate for identifying the position of the groove center. However, interference in the welding process caused the template-matching method to fail under certain conditions. Therefore, the two detection algorithms were combined to improve the detection accuracy. After filtration of the detected welding-pool center, the groove-center detection algorithm based on template matching results in higher accuracy.
This paper proposed a high-cycle fatigue life prediction method for Al-Zn-Mg alloy welded joints considering the thermal process of welding and the effect of welding pores. First, the welding process was simulated by a numerical thermal-mechanical analysis. The residual stresses were then obtained and applied as the initial stress field of the subsequent fatigue life calculation. Second, a fatigue damage model was constructed by introducing a dimensionless quantity to equivalently describe the effect of interior pores. Further, a corresponding damage mechanics-finite element numerical method was numerically implemented by the ABAQUS platform to calculate the high-cycle fatigue life for Al-Zn-Mg alloy welded joints. The calculated lives have good consistency with test data, which validates the feasibility and applicability of the proposed model and the calculation method. Finally, the curves of the cyclic stress versus fatigue life of Al-Zn-Mg alloy welded joints with different interior pore sizes were calculated to explore the influence law of the welding pore.
K E Y W O R D Sdamage mechanics-finite element numerical method, fatigue damage model, fatigue life prediction, welded joints, welding pore
As a popular technique, additive manufacturing (AM) has garnered extensive utilization in various engineering domains. Given that numerous AM metal components are exposed to fatigue loads, it is of significant importance to investigate the life prediction methodology. This study aims to investigate the high-cycle fatigue (HCF) behavior of AM AlSi10Mg, taking into account the influences of powder size and fatigue damage, and a novel ML-based approach for life prediction is presented. First, the damage-coupled constitutive model and fatigue damage model are derived, and the Particle Swarm Optimization method is employed for the material parameters’ calibration of M AlSi10Mg. Second, the numerical implementation of theoretical models is carried out via the development of a user-defined material subroutine. The predicted fatigue lives of AM AlSi10Mg with varying powder sizes fall within the triple error band, which verifies the numerical method and the calibrated material parameters. After that, the machine learning approach for HCF life prediction is presented, and the Random Forest (RF) and K-Nearest Neighbor (KNN) models are employed to predict the fatigue lives of AM AlSi10Mg. The RF model achieves a smaller MSE and a larger R2 value compared to the KNN model, signifying its superior performance in predicting the overall behavior of AM AlSi10Mg. Under the same maximum stress, a decrease in the stress ratio from 0.5 to −1 leads to a reduction in fatigue life for both powder sizes. As the powder size decreases, the rate of damage evolution accelerates, leading to shorter fatigue life.
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