The use of aluminum alloys, nowadays, is swiftly growing from the prerequisite of producing higher strength to weight ratio. Lightweight components are crucial interest in most manufacturing sectors, especially in transportation, aviation, maritime, automotive, and others. Traditional available joining methods have an adverse effect on joining these lightweight engineering materials, increasing needs for new environmentally friendly joining methods. Hence, friction stir welding (FSW) is introduced. Friction stir welding is a relatively new welding process that can produce high-quality weld joints with a lightweight and low joining cost with no waste. This paper endeavors to deals with optimizing process parameters for quality criteria on tensile and hardness strengths. Samples were taken from a 5 mm 6061-T6 aluminum alloy sheet with butt joint configuration. Controlled process parameters tool profile, rotational speed and transverse speed were utilized. The process parameters are optimized making use of the combination of Grey relation analysis method and L₉ orthogonal array. Mechanical properties of the weld joints are examined through tensile, hardness, and liquid penetrant tests at room temperature. From this research, rotational speed and traverse speed become significant parameters at a 99% confidence interval, and the joint efficiency reached 91.3%.

This article aims to study water-cooling effects on residual stress friction stir welding (FSW) of AA6068-T6 aluminum alloy. For this reason, the FSW and submerged FSW processes are simulated by computational fluid dynamic (CFD) method to study heat generation. The increment hole drilling technique was used to measure the residual stress of welded samples. The simulation results show that materials softening during the FSW process are more than submerged. This phenomenon caused the residual stress of the joint line in the submerged case to be lower than in the regular FSW joint. On the other hand, the results revealed that the maximum residual stresses in both cases are below the yielding strength of the AA6068-T6 aluminum alloy. The results indicated that the residual stress along the longitudinal direction of the joint line is much larger than the transverse direction in both samples.

Friction stir welding (FSW) of polymeric materials has recently attracted significant attention. Herein, we present the effect of the tool pin profile on the FSW of high-density polyethylene (HDPE) joints through joint experimental analysis and thermomechanical simulations. For analysis of pin profile effects on the thermomechanical properties of HDPE joints, frustum (FPT), cubic (CPT), and triangular (TPT) pin shapes were selected in this study. This research investigated the heat generation of the parts of the different tools as well as heat flux (internal and surface). The results revealed that the heat generation in pins with more edges (cubic (96 °C) and triangular (94 °C)) was greater than in pins with a smooth shape (frustum (91 °C)). The higher heat generation caused the heat flux on the surface of the HDPE from the cubic pin profile to be greater than for other joints. Due to the properties of HDPE, higher heat generation caused higher material velocity in the stirring zone, where the velocity of the materials in TPT, CPT, and FPT pins were 0.41 m/s, 0.42 m/s, and 0.4 m/s, respectively. The simulation results show sharp-edged pins, such as triangular and cubic, lead to over-stirring action and internal voids formed along the joint line. Furthermore, the simulation results indicated that the size of the stirred zones (SZs) of the FPT, TPT, and CPT samples were 17 mm², 19 mm², and 21 mm², respectively, which is around three times the corresponding values in the HAZ.

Nowadays, manufacturing sectors harness the power of machine learning and data science algorithms to make predictions of the optimization of mechanical and microstructure properties of fabricated mechanical components. The application of these algorithms reduces the experimental cost beside leads to reduce the time of experiments. The present research work is based on the depth of penetration prediction using supervised machine learning algorithms such as support vector machines (SVM), random forest algorithm, and robust regression algorithm. A friction stir spot welding (FSSW) was used to join two specimens of AA1230 aluminum alloys. The dataset consists of three input parameters: rotational speed (rpm), dwelling time (s), and axial load (kN), on which the machine learning models were trained and tested. The robust regression machine learning algorithm outperformed the rest algorithms by resulting in the coefficient of determination of 0.96. The second-best algorithm is the support vector machine algorithm, which has a value of 0.895 on the testing dataset. The research work also highlights the application of image processing techniques to find the geometrical features of the weld formation. The eroding and dilating procedures were carried out by the kernel size (3, 3) of type int 8. The results showed that the used algorithms can be considered to calculate the area, major/minor axis lengths, and the perimeter of the FSSW samples.