Xuan Van Tran

The friction stir spot welding process involves many complex problems with thermal–mechanical coupling, boundary contact, and large deformation. Among these problems, large deformation is the most difficult to deal with conventional finite element method. In this study, 3D thermal–mechanical coupling finite element analysis based on the Coupled Eulerian–Lagrangian (CEL) method is employed to simulate the friction stir spot welding process of two aluminum sheets. The computational temperature histories at different locations and the weld thermal heat-affected zones are compared with those measured from experiments. The computational results are in relatively good agreement with the experiments. The CEL approach shows no mesh distortion and can be applied for extreme deformation problems; temperature diffusion, material flows, and mixture zone results can be obtained to evaluate the quality of the joint; CEL approach is computation cost where the mesh need to be refined.

The wire arc additive manufacturing (WAAM) process involves a multitude of uncertain parameters, making WAAM a complex system for analysis. To comprehensively investigate their effects and conduct sensitivity studies, a parametric approach is essential. However, such an approach necessitates a substantial number of simulations, each of which is time-consuming and can last up to a few days. In response, we construct a deep learning-based surrogate model trained on the data created by the validated finite element (FE) method. This surrogate model is used to conduct sensitivity analysis via the SHapley Additive exPlanations (SHAP) method. The findings indicate that the positioning of the laser and its proximity to individual nodal points during the printing process are crucial features in predicting temperature evolution. Additionally, using the FFNN model instead of solely the FE model significantly reduces the computational cost (3272 times) of predicting the temperature field. This innovative approach promises to streamline the exploration of the intricate parameter space of WAAM, offering valuable insights for enhanced process optimization and control of complex manufacturing processes.

Among metallic additive manufacturing technologies, wire and arc-directed energy deposition (WADED) is recently adopted to manufacture large industrial components. In this process, controlling the temperature evolution is very important since it directly influences the quality of the deposited parts. Typically, the temperature history in WADED can be obtained through experiments and/or numerical simulations, which are generally time-consuming and expensive. In this research, we developed a robust surrogate model (SM) for predicting the temperature history in WADED based on the combination of machining learning (ML) and finite element (FE) simulation. The SM model was built to predict the temperature history in the WADED of single weld tracks. For this purpose, FE model was first developed and validated against experiments. This validated FE model is then used to generate the data to train the ML modes based on the feed-forward neural network (FFNN). The trained SM model can fast and accurately predict the temperature history in the cases which were not previously used for training with a very high accuracy of more than 99% and in a very short time with only 38 s (after being trained) as compared with 5 h for a FE model. The trained SM can be used for studies that require a large number of simulations such as uncertainty quantification or process optimization.