NDLI: A New Method for Determination of the Pre-Form Shapes and Their Corresponding Pressures in Tube-Hydroforming Process of SUS 304 in a Square Die

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Tabatabaei, S. A. Panahi, M. Shariat Tabatabaei, S. M. Mashhadi, M. Mosavi
Copyright Year	2011
Abstract	The pre-form design in hydroforming process plays a key role in improving product quality, such as defect-free property and proper final product. This approach, however, leads not only to the increase of significant tool cost but also to the extended down-time of the production equipment. It is thus necessary to reduce time and man power through an effective method of pre-form design. In this paper, the equi-potential lines designed in the electric field are introduced to find an appropriate pre-form shape. The equi-potential lines generated between two conductors of different voltages show similar trends for minimum work paths between the undeformed shape and the deformed shape. Based on this similarity, the equi-potential lines obtained by arrangement of the initial and final shapes are utilized for the design of the pre-form, and then the finite element simulations are done for finding the forming pressure of each pre-form shape. Finally, the pre-form and its corresponding forming pressure with other parameters are used for training an artificial neural network. This trained neural network can be used for estimating the proper pre-form shape and forming pressure for a SUS304 tube inside an square die or other configurations of die (Geometrical shape) and tube (Diameter and thickness).
Sponsorship	Manufacturing Engineering Division
Starting Page	503
Ending Page	515
Page Count	13
File Format	PDF
ISBN	9780791844304
DOI	10.1115/MSEC2011-50153
Volume Number	ASME 2011 International Manufacturing Science and Engineering Conference, Volume 1
Conference Proceedings	ASME 2011 International Manufacturing Science and Engineering Conference
Language	English
Publisher Date	2011-06-13
Publisher Place	Corvallis, Oregon, USA
Access Restriction	Subscribed
Subject Keyword	Neural network Tube hydroforming Equi-potential lines Pre-form Design Product quality Simulation Production equipment Artificial neural networks Downtime Shapes Engineering simulation Finite element analysis Pressure Electric fields
Content Type	Text
Resource Type	Article

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