Recent advances in springback prediction for metal tubes

Abstract Springback is a technical challenge that remains unsolved. Accurate prediction of springback is the prerequisite for effective compensation and control. Driven by advancements in new materials, processes, numerical simulation and artificial intelligence (AI), significant progress has been made in springback prediction for tubes. New challenges have arisen accordingly as well. This work focuses on springback prediction, reviewing recent advancements to fill the gap in existing literature. The role of metallurgical mechanisms and phenomena in springback are summarized. This offers a new perspective for springback studies. Research on constitutive models to improve the accuracy of springback prediction is highlighted. Theoretical frameworks for prediction have progressed beyond idealized assumptions, incorporating complex behaviors such as tension-compression (T-C) asymmetry and residual stress to establish robust springback theory, but they still rely on assumptions. An overview of the application of finite element analysis (FEA) in springback prediction and compensation is provided. FEA can deliver high fidelity under multi-axial stress, supported by constitutive models that capture phenomena such as the Bauschinger effect and T-C asymmetry. New springback compensation methods based on AI algorithms are discussed.