Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 69, Issue 9-12, Pages 2787-2792Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-013-5235-z
Keywords
Tube hydroforming; Preform design; Forming pressure; Equi-potential lines; Neural networks
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The quality of a tube-hydroformed component as well as the tooling cost and processing time involved in the process are notably affected by the component's preform. Hence, it is necessary to produce a proper preform shape for the successful hydroforming from the initial tube to final shape. In this paper, the notion of equi-potential lines (EPLs) is used to find an appropriate preform shape in the tube hydroforming process for the first time. The EPLs generated between two conductors of different voltages show minimum work paths between the initial and final shapes. Based on this similarity, the EPLs method is utilized for preform shape design. Next, the forming pressure of the preform is determined using finite element analysis. Finally, the computationally expensive procedure introduced above is significantly facilitated by employing a multi-layer perceptron neural network which is trained using results from application of the procedure to a set of uniformly distributed random input vectors. Real-world examples are presented to demonstrate the applicability and efficiency of the proposed approach.
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