Neural Networks Applied for Predictive Parameters Analysis of the Refill Friction Stir Spot Welding Process of 6061-T6 Aluminum Alloy Plates

Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed...

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Bibliographic Details
Published in:Materials 2023-06, Vol.16 (13), p.4519
Main Authors: Bîrsan, Dan Cătălin, Păunoiu, Viorel, Teodor, Virgil Gabriel
Format: Article
Language:English
Subjects:
Alloys
Aluminum
Aluminum alloys
Aluminum base alloys
Defects
Deformation
Finite element analysis
Finite element method
Friction stir welding
Friction welding
Innovations
Manufacturing
Mechanical properties
Metal plates
Metals
Neural networks
Normal stress
Optimization models
Penetration depth
Product reliability
Residual stress
Riveting
Shear strength
Simulation
Spot welding
Sustainable development
Temperature
Welding
Welding parameters
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Summary:Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma16134519