Selection of basic position in Refill Friction Stir Spot Welding of 2024-T3 and D16UTW aluminum alloy sheets

Lacki, P.; Derlatka, Anna; Gałaczyński, T.

doi:10.1515/amm-2017-0068

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…Due to its wide range of applications, this aluminum alloy is still the subject of many studies [16][17][18]. EN AW 7075 is a high-strength aluminum alloy [19] known for its excellent mechanical properties, which makes it attractive for various aerospace and automotive applications [18,[20][21][22]. FSW offers a promising way to join EN AW 7075 and other similar high-strength aluminum alloys.…”

Section: Methodsmentioning

confidence: 99%

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Lacki,

Derlatka,

Więckowski

et al. 2024

Materials

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The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. Predictive maps were developed and presented in the work, allowing for the selection of optimal parameters of the FSW process for three types of joints.

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Section: Methodsmentioning

confidence: 99%

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Lacki,

Derlatka,

Więckowski

et al. 2024

Materials

View full text Add to dashboard Cite

show abstract

“…Due to its wide range of applications, this aluminium alloy is still the subject of many studies [13][14][15]. EN AW 7075 is a high-strength aluminum alloy [16] known for its excellent mechanical properties, which makes it attractive for various aerospace and automotive applications [15,[17][18][19]. FSW offers a promising way to join EN AW 7075 and other similar high-strength aluminum alloys.…”

Section: Methodsmentioning

confidence: 99%

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Lacki,

Derlatka,

Więckowski

et al. 2024

Preprint

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The article describes machine learning using artificial neural networks (ANN) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). A total of 608 experimental data were used to build the ANN. Two types of networks were built: the first one was used to classify good/bad connections with the MLP 7-19-2 topology, the second one was used to regress the tensile load-bearing capacity with the MLP 7-19-1 topology. Selected parameters of the FSW process were used as input data for ANN training: rotational speed, welding speed, joint and tool geometry. Based on a case study, the quality of the FSW joint was assessed in terms of microstructure and mechanical properties. The usefulness of both trained neural networks has been demonstrated. For the regression network, the quality of the validation set was approximately 93.6%. In the case of confusion matrix for the test set, errors never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. On their basis, predictive maps were developed and presented in the work allowing for the selection of optimal parameters of the FSW process for three types of joints.

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“…Friction stir spot welding (FSSW) is a relatively new solidstate welding technology [7][8] in which peak temperature is lower than melting point of material, what allows for avoiding the defects in fusion welding technologies. To date, FSSW has been used in the RX-8 automobile rear wheels and some structures of Toyota Motor [9].…”

Section: Introductionmentioning

confidence: 99%

Archives of Metallurgy and Materials

Shang¹,

Zuo²,

et al. 2020

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Refill friction stir spot welding (RFSSW) was used to weld the 2060 aluminum alloy with 2 mm thickness. Joint formation, defect characteristics and mechanical properties were investigated. Results show that stir zone (SZ) is clarified into dynamic recrystallization zone (DRZ) and heat extruded zone (HEZ) due to different microstructural features. The size of void near the hook tip decreases with the increase of the plunge depth. Different hook morphologies are obtained under different plunge depths. The tensile-shear load of joint with the void defect initially decreases and then increases with increasing plunge depth. The mean loads of joints under different plunge depths are in the range of 5.1-5.8 kN. The void separates the hook from lap interface, so the cracks initiating from the hook propagate along the sleeve retreating path. The hook has a larger influence on the tensile-shear load of joint than void. All the tensile specimens present a shear-plug fracture mode.

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Selection of basic position in Refill Friction Stir Spot Welding of 2024-T3 and D16UTW aluminum alloy sheets

Cited by 13 publications

References 12 publications

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Archives of Metallurgy and Materials

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