Development on a Prediction Model for Experimental Condition of Flexibly Reconfigurable Roll Forming Process

Abstract

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology by which sheet metal is shaped into a desired curvature using reconfigurable rollers and gaps. FRRF is conducive to producing multi-curvature surfaces by controlling the longitudinal strain distribution. However, it is difficult to predict the forming results since FRRF technology forms a secondary surface from the primary curvature. This study investigates the use of regression analysis as a basis for a model that can predict the longitudinal curvature of sheet metal. The following variables were considered as input parameters: Maximum compression value, radius of curvature in the transverse direction, and initial blank width. Regression model samples are obtained by performing experiments using FRRF equipment whilst the experimental design was generated by a three-level, three-factor full factorial design. The experimental surfaces are of a convex and of a saddle-type shape, with a total sample size of 54. Through regression analysis it has been shown that the longitudinal curvature can be expressed by means of a quadratic equation. The matching quadratic function was verified with R-squared values and root-mean-square errors, whilst the normality of the sample data was also verified. To apply the model to the actual forming process, the regression model was converted to deduce the compression characteristics for forming the target surface. Throughout the study, the proposed analytical procedure was validated, and a statistical formula for estimating the longitudinal curvature produced by the FRRF apparatus established.