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An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. 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