Prediction of the impact of friction’s coefficient in cylindrical deep drawing for AA2090 Al-Li alloy using FEM and Taguchi approach

Abstract

To produce different cups from sheet metal, deep drawing is a vital process demanded by different industries such as automobile, aeronautics and so on. The comprehension of the process’s mechanics led to the correct determination of the factors which affect the deep drawing results. Experimental tests and numerical simulations are two significant ways for analysing the deep drawing process. Experimental tests are beneficial in studying the process to control the defect of the final product. However, to gain time and substantially reduce costs the numerical modelling became a common method which could be investigate the deep drawing technique through all steps of deformation. The Coulomb friction in contact regions is one of the most influencing parameters during deep drawing. This research emphasizes the influence of friction coefficient on deep drawing in three different regions: between blank surface and punch, blank surface and die, and blank surface and blank holder. For this purpose, a 3D Finite Element Model of AA2090 Al-Li alloy was carried out to simulate the process using ABAQUS software with A combination of the statistical approach based on Taguchi Techniques in order to predict the impact of friction coefficient Thickness variation have been studied, and the thinning region have been analysing for a cylindrical deep drawing process the results obtained were used as imputes of Taguchi method and the most appropriate combination of the Coulomb friction in the three regions concerning AA2090 Al-Li alloy was found.

A review of manufacturing processes, mechanical properties and precipitations for aluminum lithium alloys used in aeronautic applications

“…Bouchaâla et al. studied the influence of contact surface between the blank and the tools (Punch force, die shoulder radius, Blank holder force, Friction), two aluminum lithium alloys were used AA 2090 [ 172 ] and AA2198 [ 172 , 173 ], for this studies; a combination between FEM and Tagushi optimization was carried out [ 172 ].…”