Enhanced single point incremental forming process through sacrificial sheet usage: A hybrid optimization using design of experiments and artificial intelligence

Single Point Incremental Forming (SPIF) is an advanced and flexible sheet metal forming process that enables the production of complex geometries without the need for dedicated dies. This study investigates the application of SPIF to aluminum alloy AA1050-O using a novel sandwich configuration incorporating a sacrificial sheet placed above a skin sheet. The proposed configuration aims to improve surface quality, reduce thinning and minimize springback during forming. To evaluate the process, 2 4 full factorial design with 4 center points (L20) as implemented from the outset. A Design of Experiments (DOE) and Artificial Neural Network (ANN) approach were employed to evaluate the influence of four key process parameters; feed rate (V f ), tool diameter (D p ), vertical increment (Z), and sacrificial sheet thickness (E tm ), on three critical quality responses: surface roughness (Ra), final thickness (EP), and springback (RE). Analysis of Variance (ANOVA) was conducted to identify statistically significant factors and their interactions, with the objective of simultaneously optimizing Ra, EP, and RE. The ANN model, with a 4-8-1 architecture, demonstrated excellent predictive performance, significantly outperforming the ANOVA-based models, particularly for nonlinear responses. The prediction accuracy for approaches was assessed through the coefficient of determination ( R 2 ). The results of R 2 are 99.75% for the Ra, EP, and RE responses. The results confirm that the combined DOE–ANN hybrid approach provides a robust and effective framework for multi-objective optimization in SPIF process.