In the context of fine blanking, magnetic Barkhausen noise is to be considered a valuable source of data, for the purpose of determining mechanical properties of metals. Magnetic Barkhausen noise is a non-destructive testing procedure that has already be proven to have relationships with mechanical properties of ferromagnetic materials. Works in this field seem to indicate that machine learning is promising for estimating mechanical properties with magnetic Barkhausen noise as an input data. However, machine learning techniques, despite being able to model non-linear relationships, lack in transparency since their inner working remains opaque. It is possible, therefore, that an erroneous model, which performs well based by exploiting biases in the dataset, will be undetected.