Hardness Distribution of Al2050 Parts Fabricated Using Additive Friction Stir Deposition

Ghadimi, Hamed; Ding, Huan; Emanet, Selami; Talachian, Mojtaba; Cox, Chase; Eller, Michael R.; Guo, Shengmin

doi:10.3390/ma16031278

Cited by 13 publications

(1 citation statement)

References 28 publications

(51 reference statements)

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“…Additionally, Ghadimi et al. confirmed that the top layer of the deposited Al2050 part exhibited higher microhardness (about 118 HV) than the middle layer (about 80 HV), attributing to the re‐stirring and re‐heating of the AFSD process [8] . Therefore, it is essential to understand the effect of the thermal evolution and deformation conditions on the performance and to predict the mechanical properties with in‐process feedback control of the process parameters.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Qiao,

Liu,

et al. 2024

Materials Genome Engineering Advances

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Additive friction stir deposition (AFSD) provides strong flexibility and better performance in component design, which is controlled by the process parameters. It is an essential and difficult task to tune those parameters. The recent exploration of machine learning (ML) exhibits great potential to obtain a suitable balance between productivity and set parameters. In this study, ML techniques, including support vector machine (SVM), random forest (RF) and artificial neural network (ANN), are applied to predict the mechanical properties of the AFSD‐based AA6061 deposition. Expect for the stable parameters (temperature, force and torque) in situ monitored by the self‐developed process‐aware kit during the AFSD process and the other factors (rotation speed, traverse speed, feed rate and layer thickness) are also set as input variables. The output variables are microhardness and ultimate tensile strength (UTS). Prediction results show that the ANN model performs the best prediction accuracy with the highest R2 (0.9998) and the lowest mean absolute error (MAE, 0.0050) and root mean square error (RMSE, 0.0063). Furthermore, analysis suggests that the feed rate (24.8%/24.1%) and layer thickness (25.6%/26.6%) indicate a higher contribution that affects the mechanical properties.

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Section: Introductionmentioning

confidence: 99%