Optimizing AA3105-St12 two-layer sheet in incremental sheet forming process using neural network and multi-objective genetic algorithm

Incremental sheet forming is a flexible forming technology in which the sheet metal is gradually formed by the movement of tools in specified path. Due to the progressively localized deformation of the sheet and concentration of the forces on contact area of tool and sheet metal, the formability of the sheet increases compared with other common forming methods. In this study, numerical simulation of the incremental forming of AA3105-St12 two-layer sheet has been performed to calculate forming force and final thicknesses of the layers. The validity of the simulation results is evaluated by comparing them with those obtained from experiments. Numerical models for estimating the vertical force applied on the tool and the final thicknesses of the layers in terms of the process variables have been obtained using artificial neural network. Multi-objective optimization has been conducted to achieve the minimum force and the minimum thickness reduction of layers using obtained numerical models based on genetic algorithm method. Optimum thickness of the two-layer sheet and the thickness ratio the layers in different states of contact of the aluminum or the steel layers with the forming tool have been determined.