Experimental and numerical approaches to improve springback prediction and compensation
Published 2013-06-28
abstract views: 11 // Full text article (PDF): 10
Keywords
- sheet metal forming,
- machine learning,
- springback compensation,
- FEM simulation
How to Cite
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Striving after passenger´s safety, reduced fuel consumption and environment protection tendency lead automotive industry to development and use of new materials with higher strength. However, higher values of strength usually lead to reduced formability and increased sensitivity of springback. Today, springback is one of the most important factors that influence the quality of sheet metal forming products. We separate the following types of springback considering the geometry of the product and the forming regime: angular change, sidewall curl and twist. During the forming process, sheet metal undergoes a complicated deformation history, which is why the accurate prediction and consequently the compensation of the springback can be very difficult. In this paper the procedure for springback prediction and compensation of car body parts is presented.