Applications of Friction-Based Processes in Manufacturing

Darji, Raghavendra S.; Joshi, Gaurang R.; Badheka, Vishvesh J.; Patel, Dhiren

doi:10.1007/978-981-16-9523-0_27

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…However, in the fusion welding process for complex joints, dissimilar aluminum alloys have low weldability because of differences in chemical composition, mechanical properties, thermal expansion coefficient, and melting point which manifest as metallurgical problems such as distortion, shrinkage, and porosity resulting from the melting and solidification in the weld line [6][7][8]. Solid-state welding is a method used for welding dissimilar materials which has effective weldability and reduces problems in the weld seam because of the low melting point temperature [9][10][11]. The friction stir welding (FSW) process results in good weld quality, and the weld seam of the two materials has good mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

et al. 2023

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This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) of AA-5083 and AA-6061 friction stir welding. The machine learning model integrates two machine learning methods, Gaussian process regression (GPR) and a support vector machine (SVM), into a single model, and then uses the AMIS as the decision fusion strategy to merge SVM and GPR. The generated model was utilized to anticipate three objectives based on seven controlled/input parameters. These parameters were: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism. The effectiveness of the model was evaluated using a two-experiment framework. In the first experiment, we used two newly produced datasets, (1) the 7PI-V1 dataset and (2) the 7PI-V2 dataset, and compared the results with state-of-the-art approaches. The second experiment used existing datasets from the literature with varying base materials and parameters. The computational results revealed that the proposed method produced more accurate prediction results than the previous methods. For all datasets, the proposed strategy outperformed existing methods and state-of-the-art processes by an average of 1.35% to 6.78%.

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Section: Introductionmentioning

confidence: 99%

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

et al. 2023

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Investigating the Impact of Friction Stir Processing on the Hydrogen Embrittlement in AA6082-T6 Heat-Treatable Aluminum Alloy

Papantoniou,

Karmiris-Obratański,

Leszczyńska-Madej

et al. 2024

Met. Mater. Int.

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This study investigates the impact of friction stir processing (FSP) on the hydrogen embrittlement (HE) in AA6082-T6 heat-treatable aluminum alloy. The effects of different number of FSP passes and different hydrogen cathodic charging (HCC) conditions on the material’s response to HE are examined through comprehensive mechanical testing, microhardness analysis, and microstructural characterization. The results revealed that FSP leads to a decrease in yield strength, ultimate tensile strength, and microhardness, accompanied by an increase in energy absorption. The introduction of hydrogen through HCC significantly reduces mechanical properties, particularly in non-FSPed specimens. Notably, specimens with 8 FSP passes exhibit an interesting behavior with a slight increase in energy absorption and microhardness values after HCC. Microstructural analysis shows that FSP refines the microstructure, resulting in enhanced resistance to hydrogen-induced blistering effects. These findings contribute to the understanding of hydrogen embrittlement in FSPed aluminum alloys, providing insights for developing surface-modified materials suited for hydrogen-rich applications. Graphical Abstract

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Applications of Friction-Based Processes in Manufacturing

Cited by 2 publications

References 37 publications

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

Investigating the Impact of Friction Stir Processing on the Hydrogen Embrittlement in AA6082-T6 Heat-Treatable Aluminum Alloy

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