Achieving maximum dimensional accuracy and surface quality at the shortest possible time in single-point incremental forming via multi-objective optimization

Abstract

Single-point incremental forming is a novel and flexible method for producing three-dimensional parts from metal sheets. Although single-point incremental forming is a suitable method for rapid prototyping of sheet metal components, there are limitations and challenges facing the commercialization of this process. Dimensional accuracy, surface quality, and production time are of vital importance in any manufacturing process. The present study is aimed at selecting proper forming parameters to produce sheet metal parts which possess dimensional accuracy and good surface quality at the shortest time. Four parameters (i.e. tool diameter, tool step depth, sheet thickness, and feed rate) are chosen as design variables. These parameters are used for the modeling of the process using Group Method of Data Handing(GMDH) artificial neural networks. The data necessary for establishing empirical models are obtained from single-point incremental forming experiments carried out on a computer numerical control milling machine using central composite design. After the evaluation of the model accuracy, single- and multi-objective optimization are performed via genetic algorithm. The performance of the design variables of a tradeoff point corresponding to one of the experiments shows the efficiency and accuracy of the models and the optimization process. Considering the priorities of objective functions, a designer will be able to set proper process parameters.