A novel approach for the prediction of bend allowance in air bending and comparison with other methods

Author: Kurtaran, Hasan

Source: The International Journal of Advanced Manufacturing Technology, Volume 37, Numbers 5-6, May 2008 , pp. 486-495(10)

Publisher: Springer

Buy & download fulltext article:


Price: $47.00 plus tax (Refund Policy)


Air-bending is a major sheet-metal forming operation, where precise prediction of the developed shape is a key factor for the accuracy assessment of the final shape for the part after bending. To predict the blank shape, accurate estimation of the bend-allowance (BA) is necessary, which can be defined as the length of the un-stretched fiber at the bent state of shape. There are several different approaches to find the BA values depending on either experience-based or knowledge-based techniques. In this paper, a brief summary is provided for different approaches to find the BA values by comparing their advantages as well as, their drawbacks. They are evaluated in terms of accuracy, efficiency and ease of implementation for integrated CAD/CAM environment. Then, a novel approach; by using higher order response surface (RS) fitting for the prediction of BA during air-bending is demonstrated. This technique is in general found very promising as an integrated tool for both CAD interfaces, as well as CNC machine tools. The RS predictions, which are generated from over 1,000 bending experiments using combinations of bending radius, bending angle and material thickness, are built for different orders and are compared to Artificial Neural Network (ANN) models that are also trained by using the same experimental data.

Keywords: Air-bending; Artificial neural network; Response surface model

Document Type: Research Article

DOI: http://dx.doi.org/10.1007/s00170-007-0987-y

Affiliations: Department of Design and Manufacturing Engineering, Gebze Institute of Technology, PK. 141, Gebze-Kocaeli, Turkey, Email: hasan@gyte.edu.tr

Publication date: May 1, 2008

Related content


Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page