  Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering
applications, with rotary draw bending (RDB) the high-precision bending
processing can be achieved, however, the product will further springback. Due
to the complex structure of BMT and the high cost of dataset acquisi-tion, the
existing methods based on mechanism research and machine learn-ing cannot meet
the engineering requirements of springback prediction. Based on the preliminary
mechanism analysis, a physical logic enhanced network (PE-NET) is proposed. The
architecture includes ES-NET which equivalent the BMT to the single-layer tube,
and SP-NET for the final predic-tion of springback with sufficient single-layer
tube samples. Specifically, in the first stage, with the theory-driven
pre-exploration and the data-driven pretraining, the ES-NET and SP-NET are
constructed, respectively. In the second stage, under the physical logic, the
PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small
sample BMT dataset and composite loss function. The validity and stability of
the proposed method are verified by the FE simulation dataset, the small-sample
dataset BMT springback angle prediction is achieved, and the method potential
in inter-pretability and engineering applications are demonstrated.
