RESUMEN
A novel dual-speed tool for which the shoulder and pin rotation speeds are separately established was utilized to friction stir weld cast magnesium AZ91 with wrought aluminum 6082-T6. To assess the performance and efficacy of the dual-speed tool, baseline dissimilar welds were also fabricated using a conventional FSW tool. Optical microscopy characterized the weld microstructures, and a numerical simulation enhanced the understanding of the temperature and material flow behaviors. For both tool types, regions of the welds contained significant amounts of the AZ91 primary eutectic phase, Al12Mg17, indicating that weld zone temperatures exceeded the solidus temperature of α-Mg (470 °C). Liquation, therefore, occurred during processing with subsequent eutectic formation upon cooling below the primary eutectic temperature (437 °C). The brittle character of the eutectic phase promoted cracking in the fusion zone, and the "process window" for quality welds was narrow. For the conventional tool, offsetting to the aluminum side (advancing side) mitigated eutectic formation and improved weld quality. For the dual-speed tool, experimental trials demonstrated that separate rotation speeds for the shoulder and pin could mitigate eutectic formation and produce quality welds without an offset at relatively higher weld speeds than the conventional tool. Exploration of various weld parameters coupled with the simulation identified the bounds of a process window based on the percentage of weld cross-section exceeding the eutectic temperature and on the material flow rate at the tool trailing edge. For the dual-speed tool, a minimum flow rate of 26.0 cm3/s and a maximum percentage of the weld cross-section above the eutectic temperature of 35% produced a defect-free weld.
RESUMEN
For the friction stir welding (FSW) of AZ91 magnesium alloy, low tool rotational speeds and increased tool linear speeds (ratio 3.2) along with a larger diameter shoulder and pin are utilized. The research focused on the influence of welding forces and the characterization of the welds by light microscopy, scanning electron microscopy with an electron backscatter diffraction system (SEM-EBSD), hardness distribution across the joint cross-section, joint tensile strength, and SEM examination of fractured specimens after tensile tests. The micromechanical static tensile tests performed are unique and reveal the material strength distribution within the joint. A numerical model of the temperature distribution and material flow during joining is also presented. The work demonstrates that a good-quality joint can be obtained. A fine microstructure is formed at the weld face, containing larger precipitates of the intermetallic phase, while the weld nugget comprises larger grains. The numerical simulation correlates well with experimental measurements. On the advancing side, the hardness (approx. 60 HV0.1) and strength (approx. 150 MPa) of the weld are lower, which is also related to the lower plasticity of this region of the joint. The strength (approx. 300 MPa) in some micro-areas is significantly higher than that of the overall joint (204 MPa). This is primarily attributable to the macroscopic sample also containing material in the as-cast state, i.e., unwrought. The microprobe therefore includes less potential crack nucleation mechanisms, such as microsegregations and microshrinkage.
RESUMEN
Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.