Mathematical modelling for prediction of tube hydroforming process using RSM and ANN

P. Venkateshwar Reddy, B. Veerabhadra Reddy and P. Janaki Ramulu

International Journal of Industrial and Systems Engineering, 2020, vol. 35, issue 1, 13-27

Abstract: Tube hydroforming (THF) is a special manufacturing process used to produce tubular components having applications in aerospace and automotive industries. The present study investigates the effect of process parameters such as coefficient of friction (CF), corner radius (CR) of the die and the axial feeding (AF) of the punch. The bulge ratio and thinning ratio has been evaluated to minimise the defects like bursting, wrinkling and buckling in the tubes. Apart from many parameters, these parameters are chosen to know the effect of each individual parameter on the outcomes namely bulge ratio and thinning ratio. Each factor has varied with three levels and a total of 27 simulations were carried out based on full factorial design. RSM and ANN were applied on the obtained results in order to predict the process parameters effect on the tube hydroforming process. The R-square value of ANN (0.9524 and 0.9517) is much closure to 1 when compared to R-square value of RSM (0.9539 and 0.9509).

Keywords: tube hydroforming; THF; FEM; RSM; artificial neural network; ANN; optimisation. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=106848 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:35:y:2020:i:1:p:13-27

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().