Optimization and Analysis of Refill Friction Stir Spot Welding (RFSSW) Parameters of Dissimilar Aluminum Alloy Joints by FE and ANN Methods

• Published in: Materials Vol. 17; no. 18; p. 4586
• Main Authors: Bîrsan, Dan Cătălin, Susac, Florin, Teodor, Virgil Gabriel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.09.2024; MDPI
• Subjects: Alloying elements; Alloys; Aluminum; aluminum alloy; Aluminum alloys; Aluminum base alloys; artificial neural network; Artificial neural networks; Automation; Defects; dissimilar; Dissimilar material joining; Finite element analysis; Friction stir welding; Friction welding; Hardness; Mechanical properties; Methods; Network reliability; Neural networks; Normal stress; numerical simulation; Optimization; Penetration depth; Process parameters; RFSSW; Shear strength; Spot welding; Tensile strength; Tension tests; Thermomechanical analysis; Variance analysis; Welding; Welding parameters; artificial neural network; aluminum alloy; dissimilar; numerical simulation; RFSSW
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma17184586

The quality of the refill friction stir spot welding (RFSSW) process is heavily dependent on the selected welding parameters that influence the resultant joint characteristics. Thermomechanical phenomena integral to the process were investigated using finite element (FE) analysis on two dissimilar materials. This FE analysis was subsequently validated through controlled experiments to ensure reliability. An artificial neural network (ANN) was employed to create a neural model based on an experimental setup involving 120 different sets of welding parameters. The parameters adjusted in the experimental plan included pin penetration depth, rotational speed, retention time, and positioning relative to material hardness. To assess the neural model's accuracy, outputs such as maximum temperature and normal stress at the end of the welding process were analyzed and validated by six data sets selected for their uniform distribution across the training domain.