A computational fluid dynamics (CFD) model is presented for simulating the material flow and heat transfer in the friction stir welding (FSW) of 6061-T6 aluminum alloy (AA6061). The goal is to utilize the 3-D, numerical model to analyze the viscous and inertia loads applied to the FSW tool by varying the welding parameters. To extend the FSW process modeling, in this study, the temperature-dependant material properties as well as the stick/slip condition are considered where the material at the proximity of the FSW tool slips on the lower pressure regions. A right-handed one-way thread on a tilted FSW tool pin with a smooth, concaved shoulder is, additionally, considered to increase the accuracy of the numerical model. In addition, the viscous and frictional heating are assumed as the only sources of heat input. In the course of model verification, good agreements are found between the numerical results and the experimental investigations.

This paper discusses the detection and analysis of the acoustic emission (AE) signals to investigate the possibility of applying the AE technique for the in-process monitoring of the friction-stir-welding process. Tests are carried out for joining similar and dissimilar metals using a high-speed rotating tool under various rotational speeds and traverse speeds and for different tool penetration depths. The results of fast Fourier transform show that the amplitude of the AE signal in the frequency domain is sensitive to the change in the depth of penetration of the tool. Signals in certain frequency ranges disappear when the tool loses contact with the workpiece during the process. Discrete wavelet transform indicates significant sudden changes in the decomposed signal in the lower frequency ranges (higher levels) when the shoulder makes contact with or detaches itself from the workpieces. By identifying the frequencies during the process and analysing the wavelet decomposed signals in various levels or frequency bands, it is possible to monitor effectively the transient welding state and to identify quickly 2the process changes.

A method based on a genetically optimized neural network system (GONNS) is introduced to enhance the selection of the optimum parameters for the friction stir spot welding (FSSW) process. For a given FSSW setup, an artificial neural network (ANN) is designed with three process parameters as inputs and three process variables as outputs. The outputs of the ANN are selected as the weld's tensile force, plunging load, and process duration. Preliminary experimental results are utilized in order to train the ANN. After verifying the accuracy of the trained ANN, an optimization method based on the genetic algorithm heuristic search method is used to optimize the evaluation functions that are normalized functions of the ANN outputs. Eventually, the minimization of the evaluation functions yields the optimum ANN inputs (FSSW parameters) that are verified by additional experiments. Results affirm that the analytically obtained optimums of the FSSW parameters are valid and that, by utilizing these parameters, higher weld strength, lower plunging load, and shorter process duration are obtained.

Minimizing consumed energy in friction stir welding (FSW) is one of the prominent considerations in the process development. Modifications of the FSW tool geometry might be categorized as the initial attempt to achieve a minimum FSW effort. Advanced tool pin and shoulder features as well as a low-conductive backing plate, high-conductive FSW tools equipped with cooling fins, and single or multi-step welding processes are all carried out to achieve a flawless weld with reduced welding effort. The outcomes of these attempts are considerable, primarily when the tool pin traditional designs are replaced with threaded, Trifiute or Trivex geometries. Nevertheless, the problem remains as to how an inclined tool affects the material flow characteristics and the loads applied to the tool. It is experimentally proven that a positive rake angle facilitates the traverse motion of the FSW tool; however, few computational evidences were provided. In this study, numerical material flow and heat transfer analysis are carried out for the presumed tool rake angle ranging from −4° to 4°. Afterwards, the effects of the tool rake angle to the dynamic pressure distribution, strain-rates, and velocity profiles are numerically computed. Furthermore, coefficients of drag, lift, and side force and moment applied to the tool from the visco-plastic material region are computed for each of the tool rake angles. Eventually, this paper confirms that the rake angle dramatically affects the magnitude of the loads applied to the FSW tool, and the developed advanced numerical model might be used to find optimum tool rake angle for other aluminum alloys.

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