Bending processes is one of the important processes in sheet metal forming. One of the challenge that faces the air bending process is springback, which happens due to the elastic recovery during unloading stage. An accurate analysis of springback during the bending process is crucial to achieve a required bend angle. This paper will investigate the springback experimentally by changing many parameters such as tested material, die opening, thickness, etc. and finding its effect on the value of springback. Additionally, the paper will investigate the effect of loading time at the end of loading stage on the springback by proposing a multistage bending technique (MBT). In MBT, the loading will stop during loading stage just before the end of this stage and it will restart again shortly after. In this study, three sheet metals with different thickness will be examined, namely stainless steel, aluminium and brass. Artificial neural network (ANN) will be utilized to develop a prediction model to predict springback based on the experimental results.
|Journal||IOP Conference Series: Materials Science and Engineering|
|Publication status||Published - Mar 2 2018|
|Event||1st International Conference on Functional Materials and Chemical Engineering, ICFMCE 2017 - Dubai, United Arab Emirates|
Duration: Nov 24 2017 → Nov 26 2017
ASJC Scopus subject areas
- Materials Science(all)