A coupled thermal/material flow model of friction stir welding was developed and applied to the joining of Sc-modified aluminum alloy (7042-T6) extrusions. The model reveals that surface material is pulled from the retreating side into the weld zone where it is interleaved with in situ material. Due to frictional contact with the shoulder, the surface material is hotter than the in situ material, so that the final weld microstructure is composed of bands of material with different temperature histories. For this alloy and the associated FSW heating rates, secondary phase dissolution/precipitation temperatures are in proximity to the welding temperatures. Therefore, depending on the surface and in situ material temperatures in relation to these transformation temperatures, disparate precipitate distributions can develop in the bands of material comprising the weld nugget. Based on the numerical simulation and on thermal analysis data from differential scanning calorimetry, a mechanism for the formation of onion rings within the weld zone is presented.

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.