Application of the Taguchi Optimization Method in Order to Improve the Quality of the Conical Mini-Parts Obtained by Forming Process

Micro and mini manufacturing is becoming more important than before. Among micro and mini manufacturing processes, micro forming has economical and ecological benefits due to high production rate, low material scrap rate, net shape production, and improved mechanical properties through work hardening. Even though macro scale metal forming is well understood and has been extensively studied, these concepts cannot be applied directly to the micro scale metal forming [. In this paper, a conical mini-part was precisely evaluated from finite element (FE) simulation. The final geometry of the conical mini-part is affected by forming parameters of the deep drawing process (blankholder force, friction coefficient, speed of the deformation tools) and by the tool geometry. In order to reduce the geometry deviation, all the parameters must be studies separately to quantify their influence on the final mini-part geometry. This paper presents a study concerning the optimization of the forming process in order minimize the geometry deviation of the final parts. The main objective is to understand the factors that have the highest influence on the forming process of conical mini-parts and to modify them in such way that the resulted part is according to the designer specifications. The material used in this analysis is copper - zinc alloy with anisotropic properties. After the forming process of conical mini-parts is over and the part is removed from the forming tools, the geometry of the part is analysed and compared with the ideal shape. Due to cumulated effect of springback and other phenomena that affect the conical mini-part is not having the desired accuracy from the dimensional deviation point of view [2,. There are multiple factors that affect the mini-part geometry during forming process as: blankholder force, punch rounding radius, and side wall angle. The Dynaform 5.9.1 software was used to simulate the forming process. During optimisation process 27 simulations have been done. The part obtained after each simulation is analyzed and measured to quantify the deviation from the ideal part geometry. The presented optimization method is a good method to reduce the dimensional deviations. The advantages of this method are the reduced number of simulations tests that must be done and precision of the obtained results.