In the present research, a digital twin of coaxial one-side resistance spot welding (COS-RSW) was established for the real-time prediction of transient temperature field. A three-dimensional (3D) model of COS-RSW joint was developed based on the in-house finite element (FE) code JWRIAN-SPOT. The experimental verified FE model was employed to generate the big data of temperature of COS-RSW process. Multiple dimension interpolation was applied to process database and output prediction. The FE model can predict the thermal cycle on COS-RSW joints under different parameter couples. The interpolation effect of individual welding parameters was discussed, and a power weight judgement for welding time was essential to ensure accuracy. With the support of big data, the digital twin can provide visualization prediction of COS-RSW within 10 s, whereas numerical modeling needs at least 1 h. The proposed application of digital twin has potential to improve the efficiency of process optimization in engineering.

Coaxial one-side resistance spot welding (COS-RSW) is a newly developed process for joining metals and composites. In the present study, Al5052 and carbon-fiber-reinforced plastic (CFRP) lap joints were fabricated via COS-RSW. The welding process was modeled numerically using an in-house finite element code called JWRIAN. Single-lap shear tests were performed to evaluate the joining strength. The molten zone diameter was defined and measured experimentally to verify the numerical model. An artificial neural network (ANN) was established based on multitask learning, and its training data set was prepared via finite element analysis (FEA). The well-trained ANN was employed to generate a process window for the COS-RSW. Results demonstrated that the FEA could accurately reproduce the COS-RSW process, which served as an efficient tool for generating a process data set without performing experiments. The ANN performed multitask learning well and predicted the welding output effectively. Furthermore, Tmavg, an index representing the average value of the maximum temperature in the molten interface of CFRP, was adopted to evaluate the contribution of the integral interface temperature field to the bonding strength qualitatively. An optimal Tmavg value, which was close to the CFRP decomposition temperature of 340 °C, was obtained, and it exhibited an excellent correlation with higher bonding strengths. The process window provided welding parameters directly to yield the desired results.

A new material model of magnesium alloys, combining both Hill’48 yield function and Cazacu’06 yield function, was developed and programmed into LS-DYNA using user subroutine, in which both slip dominant and twinning/untwinning dominant hardening phenomena were included. First, a cyclic load test was performed, and its finite element analysis was carried out to verify the new material model. Then, the deformation behaviors of the magnesium crash box subjected to the compressive impact loading were investigated using the developed material model. Compared with the experimental results, the new material model accurately predicted the deformation characteristics of magnesium alloy parts. Additionally, the effect of the thickness distribution, initial deflection and contact friction coefficient in simulation models on deformation behaviors were investigated using this validated material model.