Optimization of Multiple Performance Characteristics of Cold rolling Processes Using Taguchi’s Quality Loss Function

Abstract

Listen

Translate article

The single stand cold rolling mills are generally called as reversing mills, whereby the metal strip is successively wound and unwound in the form of coil as it is repeatedly passed back and forth through the one mill stand. Rolling Pass schedule consists of setting of control parameters at each pass depending on various factor like input and output thickness, width of the coil, material to be rolled, mill capability etc. Majority Quality related problem faced by cold rolled producers using single stand cold rolling are strip thickness variation and strip flatness, also they are concerned about power consumption and production rate. This happens because there is no standard pass schedule available which can predict these Performance parameters. The operator uses thumb rule to make pass schedule. There is no Theoretical model available for such complex cold rolling process. Experimental Model using Taguchi based Single Optimization Method can improve the quality and process performance of the Mill. The main objective of this paper is to show the capability of the Taguchi Quality loss function based multi response optimization methods in increasing productivity of cold rolling process while controlling the Quality and cost of Production. The proposed methodology is used to optimize Multi process performance namely thickness variation, strip flatness, production rate and Power consumption by obtaining optimal solution for control factors exit tension, entry tension, Mill speed and Roll bending pressure for cold rolling of low carbon steel in single stand reversible cold rolling mill. A L_27 orthogonal array was selected and total 27 experiments were conducted after selecting control factors and its levels. Interaction plot shows no interaction among the control parameters. The ANOVA carried out which shows the mill speed is most significant control factor. The optimal values obtained using the multi characteristics optimization Model using Taguchi loss function has been validated by confirmation experiment.