Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: A lot of research made during these last 15 years
showed that the quantification of the springback has a significant role
in the industry of sheet metal forming. These studies were made with
the objective of finding techniques and methods to minimize or
completely avoid this permanent physical variation. Moreover, the
use of steel and aluminum alloys in the car industry and aviation
poses every day the problem of the springback. The determination in
advance of the quantity of the springback allows consequently the
design and manufacture of the tool. The aim of this paper is to study
experimentally the influence of the blank holder force BHF and the
radius of curvature of the die on the springback and their influence on
the strain in various zone of specimen.
The original of our purpose consist on tests which are ensured by
adapting a U-type stretching-bending device on a tensile testing
machine, where we studied and quantified the variation of the
springback according to displacement.