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Spring back optimization in metal forming using cohort intelligence

Published in
2015
   
Issue: 2
Pages: 2938 - 2942
Abstract

Deep drawing process is a sheet metal forming process in which the blank of sheet metal is radially drawn into a forming die by the mechanical punch. It is a shape transformation process. The process is considered "deep" drawing when the depth of the drawn part exceeds its diameter. Springback can be defined as an elastically-driven change of shape of a deformed product which takes place during removal of external loads. It is a very complex physical phenomenon which is mainly governed by the stress state obtained at the end of a deformation. The main reason for Spring Back is that as the material is bent the inner region of the bend is compressed while the outer region is stretched hence Springback occurs. Nature inspired optimization algorithms or bio – inspired algorithms have been performing very well in the field of mechanical optimization problems. Cohort intelligence is a new bio – inspired algorithm which was developed by Anand Kulkarni in 2013. It is based on the self-supervised learning behavior of cohort. Cohort refers to a group of candidates interacting and competing with one another to achieve some individual goal which is inherently common to all the candidates. The learning refers to a cohort candidate’s effort to self supervise its behavior and further adapt to the behavior of other candidate which it intends to follow. This makes every candidate to improve/evolve its own and eventually the entire cohort behaviour. In this paper Springback optimization in sheet metal forming is done using Cohort intelligence.

About the journal
JournalInternational Engineering Research Journal (IERJ)
Open AccessYes