The invention discloses a real-time prediction and control method and
system for the deformation state of a superplastic forming part based on digital twinning, and belongs to the technical field of intelligent manufacturing. The method aims at solving the technical problems that in the superplastic forming process, the deformation state of a part is invisible, control depends on experience, and quality is unstable due to open-
loop control. The core lies in that a multi-
task learning artificial intelligence prediction model taking air inflow
time sequence data as input and taking pressure in a mold cavity and a part full-field deformation state as output is constructed by fusing parameterized
finite element simulation and physical experiment data in an offline stage; in the online stage, the model is utilized to dynamically predict deformation states such as a strain field and a thickness field in the part according to air inflow data collected in real time, and process transparency is achieved; and a
model prediction control algorithm is further combined, a prediction state is compared with an ideal path, the air
inlet pressure is reversely optimized and adjusted in real time, and a closed-loop
intelligent control system is formed. According to the method, perspective and active accurate control of the internal state in the
black box forming process are achieved, and the quality consistency and the yield of parts can be improved.