the rolls as design variables. Wei and Yuying [12] studied a
Pareto-based multi-objective genetic algorithm so as to optimize
the sheet metal stamping process. Donglai et al. [13]
proposed a method, based on an adaptive response surface
model, for the optimization of sheet metal forming process
parameters and predicted the performance tolerance having
included the effects of variability of noise factors. Bonte et al.
[14] proposed a generally applicable optimization strategy for
industrial metal forming processes that makes use of FEM
simulations. Li et al. [15] developed a novel intelligent optimization
approach that integrated machine learning and