Small data-driven modeling of forming force in single point incremental forming using neural networks

Abstract

Single point incremental forming (SPIF) has revolutionized sheet shaping for small-batch production, providing an economical and effective alternative to sheet stamping and pressing, which can be cumbersome and expensive. Efficient data-driven prediction of forming forces can significantly benefit process design, development and optimization in SPIF. However, the nature of localized plastic deformation makes SPIF a time-consuming process which means it is difficult to obtain rich experimental data (or samples) in the early period of forming process design. To build an efficient data-driven model for forming force prediction using the back propagation neural networks, this paper proposes a virtual data generation approach based on mega trend diffusion function and particle swarm optimization algorithm to improve the accuracy of SPIF force prediction given small experimental data problems. The proposed modeling methodology is verified using small amount of force data obtained from pyramidal shape forming. It is found that the accuracy of the established prediction model can be improved by adding the generated virtual data to actual experimental small datasets, which provides a good predictive capability in modeling the forming force of SPIF under different process conditions.