Additive friction stir deposition of AZ31B magnesium alloy

Joshi, Sameehan S.; Patil, Shreyash M.; Mazumder, Sangram; Sharma, Shashank; Riley, Daniel A.; Dowden, Shelden; Banerjee, Rajarshi; Dahotre, Narendra B.

doi:10.1016/j.jma.2022.03.011

Cited by 49 publications

(16 citation statements)

References 76 publications

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“…On the contrary, the scarcity of mechanical twins in 116 J/mm sample suggests deformation majorly via slip. Moreover, the strain rate generated during the 116 J/mm sample fabrication due to lower linear velocity is likely to be lower 37 . Also, the longer duration of thermal cycles associated with the fabrication of 116 J/mm sample sustains the heat for a longer duration (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…AZ31B-Mg plate was utilized as the substrate plate during AFSD. It is worth noting here that, the current study formed as a continuation effort of the previous publication by the authors related to the process optimization aspects of the AFSD fabrication of AZ31B Mg alloy 37 . Several preliminary trials were conducted to carry out AFSD of AZ31B Mg material to select the AFSD process parameters leading to successful AFSD fabrication of AZ31B Mg.…”

Section: Methodsmentioning

confidence: 98%

“…1 b. Further details about the computations of heat and energy inputs during AFSD process can be located in previous publication by the present research group 37 .…”

Section: Methodsmentioning

confidence: 99%

“…Thus, in case of sliding/sticking regime the value of ranges anywhere from 1 to 0. The term M is derived from back calculation using experimentally obtained tool torque data during deposition 37 , as explained below where is mechanical efficiency. Furthermore, the slip rate 49 can be expressed as where is a scaling constant and is the reference value for the rotational tool speed.…”

Section: Methodsmentioning

confidence: 99%

“…The observed property response was analyzed based on the micro and nano scale structural changes experienced by the AFSD processed material compared to the feed stock. The current work formed as a part of continuation of efforts by the present research group focusing on the advanced processing of the Mg alloys 2 , 6 , 29 – 37 .…”

Section: Introductionmentioning

confidence: 99%

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A multi modal approach to microstructure evolution and mechanical response of additive friction stir deposited AZ31B Mg alloy

Joshi

Sharma

Radhakrishnan

et al. 2022

Sci Rep

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Current work explored solid-state additive manufacturing of AZ31B-Mg alloy using additive friction stir deposition. Samples with relative densities ≥ 99.4% were additively produced. Spatial and temporal evolution of temperature during additive friction stir deposition was predicted using multi-layer computational process model. Microstructural evolution in the additively fabricated samples was examined using electron back scatter diffraction and high-resolution transmission electron microscopy. Mechanical properties of the additive samples were evaluated by non-destructive effective bulk modulus elastography and destructive uni-axial tensile testing. Additively produced samples experienced evolution of predominantly basal texture on the top surface and a marginal increase in the grain size compared to feed stock. Transmission electron microscopy shed light on fine scale precipitation of Mg$$_{17}$$ 17 Al$$_{12}$$ 12 within feed stock and additive samples. The fraction of Mg$$_{17}$$ 17 Al$$_{12}$$ 12 reduced in the additively produced samples compared to feed stock. The bulk dynamic modulus of the additive samples was slightly lower than the feed stock. There was a $$\sim\,$$ ∼ 30 MPa reduction in 0.2% proof stress and a 10–30 MPa reduction in ultimate tensile strength for the additively produced samples compared to feed stock. The elongation of the additive samples was 4–10% lower than feed stock. Such a property response for additive friction stir deposited AZ31B-Mg alloy was realized through distinct thermokinetics driven multi-scale microstructure evolution.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 98%

“…1 b. Further details about the computations of heat and energy inputs during AFSD process can be located in previous publication by the present research group 37 .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A multi modal approach to microstructure evolution and mechanical response of additive friction stir deposited AZ31B Mg alloy

Joshi

Sharma

Radhakrishnan

et al. 2022

Sci Rep

Self Cite

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Mechanical Properties of Additive Friction Stir Deposits

Avery,

Perry

2024

Solid‐State Metal Additive Manufacturing

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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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Additive friction stir deposition of AZ31B magnesium alloy

Cited by 49 publications

References 76 publications

A multi modal approach to microstructure evolution and mechanical response of additive friction stir deposited AZ31B Mg alloy

A multi modal approach to microstructure evolution and mechanical response of additive friction stir deposited AZ31B Mg alloy

Mechanical Properties of Additive Friction Stir Deposits

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

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