Stochastic Tensile Failure Analysis on Dissimilar AA6061-T6 with AA7075-T6 Friction Stir Welded Joints and Predictive Modeling

Rao, Thella Babu

doi:10.1007/s11668-020-00937-3

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Hence, the elongation at break (fracture strain) increased from around 5.5% in the case of hot-welded sample to around 5.9% in the case of cold-welded sample, which represent an increase of more than 7% by increasing the welding speed from 70 mm/min to 130 mm/min. This decline in ductility for the hot-welded samples aligns with findings from Darzi Naghibi et al [29], and Babu Rao [8] who previously reported a significant hardness mismatch in these particular welds. This phenomenon highlights the significant impact of process conditions on the mechanical behavior of the resulting joints, particularly in terms of ductility.…”

Section: Tensile Testsupporting

confidence: 89%

“…This work emphasized on optimization strategies and tool-related considerations in FSW processes. Babu Rao et al [8] employed stochastic analysis techniques to evaluate the variability in the tensile strength of the welded lap joints of AA 7075-T6, The lowest tensile strength was obtained for the weld realized by the straight cylindrical profiled tool. Additionally, the tensile strength increased by increasing the tool rotational speed up to 2000 rpm.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Thermomechanical Behavior and Microstructure of AA 7075-T6 Aluminum Alloy Lap Joints at Optimal Predicted FSW Process Parameters

Toumi,

Khalifa,

Silvestri

et al. 2024

Preprint

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Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

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Section: Tensile Testsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Assessment of the Thermomechanical Behavior and Microstructure of AA 7075-T6 Aluminum Alloy Lap Joints at Optimal Predicted FSW Process Parameters

Toumi,

Khalifa,

Silvestri

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In his investigation of the stochastic performance of AA6061-T6 and AA7075, Thella Babu reported that the UTS and hardness developed up to 2000 rpm but thereafter slumped owing to voids in the stir zone (SZ) induced by the excessive stirring. The ML models were 95.91 percent precise, with an estimated margin of error of 3.18 percent [12]. The Pearson VII universal kernel (PUK) boosted the SVM, and Gaussian process regression (GPR) boosted the same radial basis function (RBF) performance compared to poly-kernel (PK) in predicting the UTS.…”

Section: Introductionmentioning

confidence: 99%

Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies

2024

Eng. Res. Express

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Employing tailor-made alloys with uneven thickness achieves light weighting, a critical issue for reducing emissions, leading to lower aircraft pollutants and fuel costs. The research utilizes advanced machine learning techniques such as Gaussian process regression (GPR), artificial neural networks (ANN) linear regression (LR), and support vector machines (SVM) to predict the ultimate tensile strength of underwater friction stir welding of AA6082-T6 and A2219-T83 tailor-made joints. The models have been evaluated with an assortment of kernel functions, including the polynomial kernel (PK), the radial basis function (RBF), and the Pearson VII universal kernel (PUK). To acquire experimental data, we used a Central Composite Design (CCD) technique, incorporating various factors in the process encompassing tool tilt angle (TA), rotating speed (RS), and welding speed (WS). The SVM radial basis function model (SRBP) had a maximum correlation coefficient of 0.9995 and a minimum root mean square error value (RMSE) of 0.5433 in the training set and 0.6271 in the test set. The ANN model predicted the UTS with an error margin of 0.21%, while the SRBP model showed a 0.52% error, and the LR model exhibited a significantly higher error of 7.73%. A peak tensile strength of 252.98 MPa was recorded in the S20 specimen, accounting for 85.61% of the base metal's (AA6082 T6) strength. A reduced acute tearing ridge indicates petite, shallow dimples due to the inherent cooling. Through the analysis of metrics and residuals, high accuracy rates were observed when employing the ANN and SRBP models to predict mechanical traits.

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Machine Learning Prediction of Aluminum Alloy Stress–Strain Curves at Variable Temperatures with Failure Analysis

Dorbane,

Harrou,

Anghel

et al. 2023

J Fail. Anal. and Preven.

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Stochastic Tensile Failure Analysis on Dissimilar AA6061-T6 with AA7075-T6 Friction Stir Welded Joints and Predictive Modeling

Cited by 7 publications

References 43 publications

Assessment of the Thermomechanical Behavior and Microstructure of AA 7075-T6 Aluminum Alloy Lap Joints at Optimal Predicted FSW Process Parameters

Assessment of the Thermomechanical Behavior and Microstructure of AA 7075-T6 Aluminum Alloy Lap Joints at Optimal Predicted FSW Process Parameters

Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies

Machine Learning Prediction of Aluminum Alloy Stress–Strain Curves at Variable Temperatures with Failure Analysis

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