Springback Prediction

Springback Prediction is a computational modeling method in sheet metal stamping processes that quantifies the elastic recovery of formed metal components after tool release, distinguishing itself by enabling accurate a priori estimation of geometric deviations to improve die design and dimensional accuracy.

489

Publications

18.8K

Citations

1.2K

Authors

388

Institutions

Publications per year

2017–2026

144

Authors

1.2K

  1. ML
    Myoung‐Gyu Lee

    Seoul National University, South Korea

    17 publications · 1.3K citations

    16

    h-index

  2. FB
    F. Barlat

    Pohang University of Science and Technology, South Korea

    13 publications · 654 citations

    13

    h-index

  3. ML
    Michael L. Wenner

    General Motors (United States), United States

    10 publications · 929 citations

    10

    h-index

  4. KC
    Kwansoo Chung

    Seoul National University, South Korea

    11 publications · 480 citations

    10

    h-index

  5. DK
    Dae-Yong Kim

    Seoul National University, South Korea

    8 publications · 838 citations

    8

    h-index

1–5 of 1.2K

Venues

  1. Journal of Materials Processing Technology

    57 publications · 3.4K citations

    36

    h-index

  2. International Journal of Mechanical Sciences

    40 publications · 2.5K citations

    32

    h-index

  3. 23

    h-index

  4. Materials & Design (1980-2015)

    20 publications · 1.2K citations

    19

    h-index

  5. International Journal of Material Forming

    23 publications · 546 citations

    15

    h-index

1–5