PREDICTION AND ENHANCEMENT OF THICKNESS REDUCTION IN MULTI-POINT FORMING PROCESS USING ANOVA ALGORITHM

Abstract

Multipoint forming is an engineering concept which means that the working surface of the die
and/or punch is made up of hemispherical ends of individual active elements (called pins), where
each pin can be independently, vertically displaced using a geometrically reconfigurable die,
precious production time is saved because several different products can be made without
changing tools. The aim of this work is to present the effect of many parameters (blank Holder
types, rubber thickness and forming speed) on the reduction of thickness for brass with 0.71 mm
thickness. This research is concentrate on the development of predictive models to estimate the
minimum deviation in thickness using analysis of variance (ANOVA), minimum thickness
deviation has been modeled. In the development of this predictive model, blank holder, rubber
thickness and forming speed have been considered as model parameters. Arithmetic the
minimum thickness deviation used as response parameter to assess the thickness reduction of
Multipoint forming parts. The data required has been generated, compared and evaluated to the
proposed models that obtained from experiments. Taguchi algorithm is used to predict the
effect of forming parameters on thickness reduction in forming process of Brass (65-35) based
on orthogonal array of L9. The analysis of variance was used to find the best factors that effect
on the thickness deviation, The result of this research is the contribution of blank holder types,
rubber thickness and forming speed with respect to minimum thickness deviation is (69.195,
18.1 and 12.733) % respectively.