The paper presents the results of neural network sensitivity analysis used in prediction system of tool durability in die forging processes. Data collected during many experiments, tabulated in the form of knowledge vectors, has been used as a source of training data for artificial neural networks. The sensitivity analysis makes it possible to differentiate between the important variables and those which do not make a significant contribution to the results of the network operation. The obtained results of global sensitivity analysis, conducted for the elaborated network in the context of predicting the life of forging tools from the expert viewpoint, indicate general correctness and validity of the adopted model (solution), ascribing the highest sensitivity to the nitritiding input variable (related to hardness), which is in reality the main factor determining the tool resistance to the destructive effect of failure mechanisms.Keywords: artificial neural network, decision support system, durability of forging tools
© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.