As the central component in friction stir welding, the design and manufacture of welding tools for aluminum alloys have garnered substantial attention. However, the understanding of tool reliability during the welding process, especially in terms of fatigue performance, remains unclear. This paper focuses on the welding of AA2219-T4 as a case study to elucidate the predominant failure mode of the tool during the friction stir welding (FSW) of aluminum alloys. Experimental methods, including FSW welding and fracture morphology analysis of the failed tool, coupled with numerical simulation, confirm that high-cycle mechanical fatigue fracture is the primary mode of the tool failure. Failures predominantly occur at the tool pin's root and the shoulder end face with scroll concave grooves. The experimental and simulation results exhibit a noteworthy agreement, validating the reliability of the simulation model. The FSW Arbitrary Lagrangian-Eulerian (ALE) model developed in this study analyzes stress distribution and variation under the thermo-mechanical coupling effect of the tool. It reveals that stress concentration resulting from structural changes in the tool is the primary driver of fatigue crack initiation. This is attributed to exposure to alternating cyclic stresses such as bending, tension, and torsion at the tool pin's root, manifesting as multiaxial composite mechanical fatigue. Among these stresses, bending alternating cyclic stress exerts the most significant influence. The paper employs the Tool Life module in DEFORM software to predict the fatigue life of the tool. Results indicate that reducing welding speed or increasing rotation speed can enhance the tool's fatigue life to some extent. The methodology proposed in this paper serves as a valuable reference for optimizing FSW structures or processes to enhance the fatigue performance of welding tools.
In advanced solid-state manufacturing processes such as friction stir welding, the metal's temperature ranges from room temperature to the solidus temperature. The material strength in the temperature range is generally required for investigating the mechanical behaviors. In this communication paper, an analytical model is proposed for describing the thermal softening of aluminum alloys for room temperature to solidus temperature, in which the concept of temperature-dependent transition between two thermal softening regimes is implemented. It is demonstrated that the proposed model compares favorably to the well-known Sellars-Tegart model and Johnson-Cook model. The constants of the proposed model for nine typical engineering commercial aluminum alloys are documented.
Unlike the conventional fusion welding process, friction stir welding (FSW) relies on solid-state bonding (SSB) to join metal surfaces. In this study, a straightforward computational methodology is proposed for predicting the material bonding defects during FSW using quantitative evaluation of the in-process thermal-mechanical condition. Several key modeling methods are integrated for predicting the material bonding defects. FSW of AA2024 is taken as an example to demonstrate the performance of the computational analysis. The dynamic sticking (DS) model is shown to be able to predict the geometry of the rotating flow zone near the welding tool. Butting interface tracking (BIT) analysis shows a significant orientation change occurring to the original butting interface, owing to the material flow in FSW, which has a major impact on the bonding pressure at the butting interface. The evolution of the interfacial temperature and the interfacial pressure at the butting interface was obtained to analyze their roles in the formation of material bonding. Four bonding-quality indexes for quantifying the thermal-mechanical condition are tested to show their performance in characterizing the bonding quality during FSW. When the BQI is below a critical value, a bonding defect will be generated. The paper indicates that the simulation-based prediction of a material bonding defect is highly feasible if the developed methodology is extended to quantitatively determine the critical value of the bonding quality index for successful SSB for various alloys.
Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.
