Application of Machine Learning for Prediction of Springback in S-Rail Sheet Metal Forming

DOI

10.2991/978-94-6239-750-7_11How to use a DOI?

Keywords

Sheet metal; forming; S-rail; FEM simulation; machine learning springback; gradient

Abstract

Sheet metal operations experienced different defects of forming such as springback. Since Sheet metal forming is complex and multi variable dependent problem, hence it is necessary to avoid such defects such springback at design stage. For remedy of such forming defects both FEA and machine learning are applied in sheet metal forming. The objective of current work is to predict springback developed during S-rail forming through Gradient Boosting and XG Boost algorithms. For prediction of springback angle developed during S-rail forming, results of FEM simulation of S-rail forming were referred from previous studies for formulation of machine learning model formulation. Gradient Boosting and XG Boost, when trained with FEM-derived datasets, offer highly accurate, data-driven alternatives to traditional and even previously employed machine learning models for springback prediction. Gradient boosting found to be more accurate as compared to XG Boost due to higher R2 Score and lower errors values. Both models display close agreement with FEM simulations, validating their suitability for rapid, reliable, and cost-effective prediction needed for manufacturing process optimization.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yogesh Dewang
AU  - Sachin Kumar Nikam
AU  - Suraj Prasad
AU  - Shashi Ranjan Mohan
AU  - Lokesh Sharma
PY  - 2026
DA  - 2026/08/31
TI  - Application of Machine Learning for Prediction of Springback in S-Rail Sheet Metal Forming
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 145
EP  - 162
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_11
DO  - 10.2991/978-94-6239-750-7_11
ID  - Dewang2026
ER  -