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Article Dans Une Revue EPJ N - Nuclear Sciences & Technologies Année : 2016

Modelling of powder die compaction for press cycle optimization

Résumé

A new electromechanical press for fuel pellet manufacturing was built last year in partnership between CEA-Marcoule and Champalle$^{Alcen}$. This press was developed to shape pellets in a hot cell via remote handling. It has been qualified to show its robustness and to optimize the compaction cycle, thus obtaining a better sintered pellet profile and limiting damage. We will show you how 400 annular pellets have been produced with good geometry's parameters, based on press settings management. These results are according to a good phenomenological pressing knowledge with Finite Element Modeling calculation. Therefore, during die pressing, a modification in the punch displacement sequence induces fluctuation in the axial distribution of frictional forces. The green pellet stress and density gradients are based on these frictional forces between powder and tool, and between grains in the powder, influencing the shape of the pellet after sintering. The pellet shape and diameter tolerances must be minimized to avoid the need for grinding operations. To find the best parameters for the press settings, which enable optimization, FEM calculations were used and different compaction models compared to give the best calculation/physical trial comparisons. These simulations were then used to predict the impact of different parameters when there is a change in the type of powder and the pellet size, or when the behavior of the press changes during the compaction time. In 2016, it is planned to set up the press in a glove box for UO$_2$ manufacturing qualification based on our simulation methodology, before actual hot cell trials in the future.

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Matériaux
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cea-01350711 , version 1 (02-10-2019)

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Jean-Philippe Bayle, Vincent Reynaud, François Gobin, Christophe Brenneis, Eric Tronche, et al.. Modelling of powder die compaction for press cycle optimization. EPJ N - Nuclear Sciences & Technologies, 2016, 2, pp.25. ⟨10.1051/epjn/2016018⟩. ⟨cea-01350711⟩

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