Mastering the Complexity of Incremental Forming: Geometry-Based Accuracy Prediction Using Machine Learning †

Abstract

The envisaged flexibility of Single Point Incremental Forming is contradicted by its highly complex deformation behavior, making the process easy to implement but difficult to fully control. This paper describes a regression method that uses Gradient Tree Boosting to predict the deviations for a given input geometry, which can replace the physical part production needed for the optimization of generating toolpaths. This paper elaborates on the calculation of the geometric features used by the regressor and the selection of an appropriate training dataset. The method is validated using a generated dataset of fully freeform ellipsoid workpiece geometries.