The faults in welding design and process every so often yield defective parts during friction stir welding (FSW). The development of numerical approaches including the finite element method (FEM) provides a way to draw a process paradigm before any physical implementation. It is not practical to simulate all possible designs to identify the optimal FSW practice due to the inefficiency associated with concurrent modeling of material flow and heat dissipation throughout the FSW. This study intends to develop a computational workflow based on the mesh-free FEM framework named smoothed particle hydrodynamics (SPH) which was integrated with adaptive neuro-fuzzy inference system (ANFIS) to evaluate the residual stress in the FSW process. An integrated SPH and ANFIS methodology was established and the well-trained ANIS was then used to predict how the FSW process depends on its parameters. To verify the SPH calculation, an itemized FSW case was performed on AZ91 Mg alloy and the induced residual stress was measured by ultrasonic testing. The suggested methodology can efficiently predict the residual stress distribution throughout friction stir welding of AZ91 alloy.

The mechanical properties of a magnesium alloy strongly depend on its grain structure. It is desirable to minimize grain growth during the post-forming annealing treatment, thereby minimizing the loss in strength while regaining ductility. In the present research, the annealing of the hot-rolled AZ61 Mg alloy at different temperatures for different times was performed to reveal the kinetics of grain growth, as affected by the precipitation or dissolution of second-phase particles. Three approaches, i.e., experimental, analytical modeling and atomistic simulation were taken and the results were compared. The predictions made from the analytical model and Monte Carlo simulation were both in acceptable agreement with experimental results in terms of the resultant grain sizes. However, the Monte Carlo simulation showed advantages over the analytical model. It was found that with increasing annealing temperature and holding time, the fraction of second-phase particles reduced, which strongly affected the kinetics of grain growth, limiting grain size and grain size homogeneity. The average grain sizes and the largest grain sizes were both taken as the characteristic parameters of the as-annealed microstructure. The results pointed out the importance of choosing an appropriate combination of annealing temperature and time in order to retain second-phase particles during annealing not only for preventing unrestricted grain growth but also for avoiding grain size inhomogeneity.

This research was aimed to assess the potential of a radial basis function (RBF) approximation method against the dislocation substructure-based constitutive model in predicting high-temperature deformation behavior of the AA7020 aluminum alloy. Hot compression tests were performed over a range of strain rate of 0.1–100 s⁻¹ and a range of temperature of 350–500 °C up to a strain of 0.6. The hot deformation behavior of the alloy was first described by a substructure kinetic-based constitutive equation, with the effects of strain, strain rate and temperature together with dynamic recovery parameters taken into consideration. A RBF approximation method was then developed to model the flow behavior of the material. The RBF model, as a kind of novel mesh-free function estimation approach, was trained and tested with the obtained datasets from the hot compression tests. The performance of the developed analytical and neural computational models was evaluated using statistical criteria. The results showed that the RBF model was more proficient and accurate in predicting the hot deformation behavior of this aluminum alloy than the substructure-based constitutive model.