2021
DOI: 10.1016/j.cirpj.2021.05.007
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An artificial neural network model for multi-pass tool pin varying FSW of AA5086-H34 plates reinforced with Al2O3 nanoparticles and optimization for tool design insight

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Cited by 22 publications
(17 citation statements)
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“…In the last stage of their work, the nano-sized SiC particle was reinforced by considering the optimal values obtained from previous stages. The tensile properties for the specimen reinforced with the nano-SiC particles were the 32,81 highest (UTS = 237.77 ± 10 MPa and YS = 176.67 ± 10 MPa) of all other welded specimens, which are attributed to the pinning effect. Also, the highest toughness and higher hardness were recorded for the samples incorporated with the nano-SiC particles.…”
Section: Tensile Propertiesmentioning
confidence: 89%
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“…In the last stage of their work, the nano-sized SiC particle was reinforced by considering the optimal values obtained from previous stages. The tensile properties for the specimen reinforced with the nano-SiC particles were the 32,81 highest (UTS = 237.77 ± 10 MPa and YS = 176.67 ± 10 MPa) of all other welded specimens, which are attributed to the pinning effect. Also, the highest toughness and higher hardness were recorded for the samples incorporated with the nano-SiC particles.…”
Section: Tensile Propertiesmentioning
confidence: 89%
“…The grain refinement in the SZ is due to the dynamic recrystallization (DRX) that transformed stretched microstructure into equiaxed grains. 32,[111][112][113] The addition of reinforcing particles exhibited even finer grain structures due to the effect of reinforcing particles themselves and DRX from the FSW process. 34 Moreover, the particles interact with matrix material and increase the nucleation sites by breaking the initial grains that restrain the grain boundary movement.…”
Section: Microstructural Characterizationmentioning
confidence: 99%
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