A heavy-lift launch vehicle is the basic launch vehicle for the long-term development plans for spaceflight and deep space exploration. A rocket fuel tank is the main load-bearing structure of a rocket, which has extremely high requirements for manufacturing quality and reliability. The advanced rocket fuel tank is made of 2219 high-strength aluminum alloy with a thickness of 18mm. Friction stir welding (FSW) has the advantages of no need to add welding wire, no shielding gas, no pollution, no smoke, no radiation, high welding efficiency, small product deformation after welding, and excellent weld mechanical properties and has become the main connection process for the rocket fuel tanks .
The whole process of FSW includes three phases: plunging and dwelling, welding, and tool withdrawal phase. The plunging and dwelling phase is the initial phase, which will affect the subsequent welding phase. In this phase, the pin is slowly pressed into the weldment. The plasticization and flow behavior of the weldment material near the pin is the basis for the formation of the joint. Improper selection of welding parameters during plunging and dwelling phase of FSW 18mm thick 2219 aluminum alloy will result in high temperature gradient and uneven temperature distribution along the thickness of the weldment, and thus leads to defects in welded joints, such as flashes, holes, incomplete penetration, etc.. Welding defects directly affects the mechanical properties of the weldment. The thermo mechanical affect zone and weld nugget zone of the weldment are defined as the welding core area. The material flow in this area is intense, which directly affects the weld morphology and welding quality. It is difficult to measure the temperature distribution in the welding core area due to the tool rotation, shielding of the shoulder, material flow, and severe plastic deformation in the welding zone. Therefore, finite element method (FEM) has become an important means to study the temperature distribution in the welding core area. At present, software such as ANSYS, MSC. Marc, FLUENT, ABAQUS and DEFORM are widely adopted to simulate the FSW process.
Scholars have simulated temperature distribution in the welding phase of FSW based on heat source model. McClure et al. , Jiang et al.  and Liu et al.  used the Rosenthal analytical method to analyze the transient temperature distribution in FSW process, and obtained the thermal cycle curve of each feature point in the welding zone. He et al.  and Wan et al.  used MSC. Marc established FSW simulation models, analyzed the ultrasonic-assisted FSW and FSW, and studied the temperature distribution during the welding process. Ren et al.  and Xu et al.  established FSW heat source model based on the torque heat source model, and used ANSYS to study the temperature distribution of the welding process and the residual stress after welding. Most scholars simulated FSW process based on heat source model, and studied the influence of welding parameters on temperature distribution. Complex nonlinear friction heat and plastic deformation heat in FSW process make it difficult to use analytical methods to describe temperature distribution.
Scholars have simulated temperature distribution in the welding phase of FSW based on FLUENT and ABAQUS. Eyvazian et al.  and Feng et al.  used FLUENT to simulate FSW process of dissimilar and same materials, and studied the temperature distribution of weldments. Yang et al.  used FLUENT to establish a simulation model for friction stir lap welding of dissimilar materials of Q235 steel and 6061 aluminum alloy, and studied the influence of different welding process parameters on the temperature distribution. Su et al.  and Yang et al.  established FSW simulation models of different stir pin shapes based on CFD method, and studied the effects of stir pin shape, shoulder radius, rotational speed and welding speed on temperature distribution in the welding process. The simulation method based on CFD has no strict restriction on the mesh size, but it cannot simulate the plunging and dwelling phase and the tool withdrawal phase, only the welding phase. Some scholars used ABAQUS to simulate FSW process, and studied the influence of size parameters of the tool and the welding parameters on temperature distribution of the weldment. Zhang et al. , Iordache et al.  and Liu et al.  used the Arbitrary Lagrangian-Euler (ALE) method in ABAQUS to avoid mesh loss, established FSW simulation model, and studied the velocity distribution, temperature distribution and equivalent plastic strain distribution during the welding phase, and studied the influence of different tool size parameters on the temperature distribution. ALE method usually ignores the simulation of plunging and dwelling phase to reduce the deformation of the mesh, and is difficult to simulate the whole process of FSW medium thickness weldment.
DEFORM attracted the attention of many scholars to simulate temperature distribution of FSW. Zhou et al.  and Han et al.  simulated the FSW process using local mesh refinement and adaptive following technology, obtained the temperature, strain distribution and material flow law of the welding process, and studied the influence of the rotational speed, welding speed and down pressure on the temperature distribution. Asadi et al.  used DEFORM to study FSW process of magnesium alloy, and adopted point tracking method to study the temperature distribution and material flow in the welding process. DEFORM has strong mesh refinement capability, which reduces the total number of meshes and shortens the computation time. In the process of simulation, if the mesh deformation reaches a certain degree, DEFORM will automatically re-divide the global meshes to make the model easier to converge. Most notably, DEFORM can simulate the whole phase of FSW.
Scholars have carried out research on temperature distribution in the welding phase, and have drawn many meaningful conclusions. However, no research on the optimization of welding parameters in the plunging and dwelling phase has been done. There are many welding parameters in the plunging and dwelling phase, such as the rotational speed, the press amount, the tool tilt angle, the plunging traverse speed and the dwelling time. The influence law of the contributing factors on temperature are still blank. Different welding parameters are suitable for weldments of different materials and sizes. Therefore, it is necessary to study the influence of welding process parameters on the temperature field of FSW 18mm thick 2219 aluminum alloy during the plunging and dwelling phase, and to optimize the welding parameters. To realize the prediction of temperature distribution of FSW 18mm thick 2219 aluminum alloy and the optimization of the welding parameters, a simulation model of FSW is built based on DEFORM. The validity of the simulation results is verified by experiments. With the minimum temperature difference in the core area of the weldment as the target value, and the weldable temperature range of 2219 aluminum alloy as the constraint conditions, orthogonal experiments and variance analyses are carried out. The research determines the significant influence factors of the temperature difference and achieves the optimization of welding parameters in the plunging and dwelling phase.