Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed ( ), plunge force ( ), and welding speed ( ) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables ( , , ,) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Friction stir welding (FSW) is a solid-state joining process, where joint properties largely depend on the amount of heat generation during the welding process. The objective of this paper was to develop a numerical thermomechanical model for FSW of aluminum-copper alloy AA2219 and analyze heat generation during the welding process. The thermomechanical model has been developed utilizing ANSYS Ò APDL. The model was verified by comparing simulated temperature profile of three different weld schedules (i.e., different combinations of weld parameters in real weld situations) from simulation with experimental results. Furthermore, the verified model was used to analyze the effect of different weld parameters on heat generation. Among all the weld parameters, the effect of rotational speed on heat generation is the highest.

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Non-destructive evaluation (NDE) techniques of phased array ultrasonic testing (PAUT) and digital x-ray radiography were employed on friction stir (FS) welded Aluminum Alloy (AA) 2219-T87 specimens. PAUT intricacies are discussed which are required for scanning of FS welded specimens with a 10 MHz 32-element transducer. The "time corrected gain" (TCG) calibration is required for scanning with an increase in index offset to compensate for decrease in A-Scan signal peak amplitude. Calibration techniques to find small defects with appropriate size tolerances are also established. The NDE technique of digital X-ray radiography is compared to PAUT where it was found that a calibrated PAUT system is able to discover defects less than 0.2 mm where X-ray radiography could not. Incomplete penetration (IP), wormhole (WH), surface cavity (SC), and internal void (IV) defects are analyzed. Furthermore, an on-line PAUT system for FSW has been developed and successfully tested. The work provided herein will provide a gateway for an ultimate goal of an automated PAUT on-line sensing system.

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