The article presents the results of a sensitivity analysis of artificial neural networks developed for a system which predicts the durability of forging tools used in the selected hot die forging process. The developed system makes it possible to calculate the geometric loss of the examined tool for the given values of its operating parameters (number of forgings, tool temperature at selected points, type of the applied protective layer, pressure and path of friction) and estimates the intensity of the occurrence of typical mechanisms of tool destruction, i.e. thermo-mechanical fatigue, mechanical wear, abrasive wear and plastic deformation. Nine neural networks operate in the developed system. Five of them determine the geometric loss of the material used for tools operating with protective layers, including a nitrided layer, a pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN. Four networks make calculations determining the intensity of the occurrence of typical destructive mechanisms. The developed sensitivity analysis allows for each neural network to show which input parameters are most important and have the greatest impact on the explained variables. This is determined based on the network error analysis in the case of elimination of individual variables from the input data. The greater the network error calculated after rejecting an input variable relative to the error obtained for the network with all the input variables, the more sensitive the network to the lack of this variable. The best compliance was obtained for the first developed set of networks regarding the geometric loss of material, while the lowest compliance was obtained for the second developed set of networks regarding the applied protective layers, and in particular for plastic deformation and mechanical fatigue, probably due to the smallest size of these sets in the knowledge base. The obtained results of this analysis are important for the system operation, i.e. supporting the technologist’s decision in the selection of such process parameter values that will increase the die’s lifetime.

The work presents the possibilities of applying modern measuring techniques, classic and coordinate measuring techniques in the forging industry (various stages of technological sequence), with the use of various measuring tools, instruments and machines applied in workshop metrology. The work points to different aspects of measuring techniques used mainly for two groups of objects, emphasizing their important role in the context of safety (achieved forgings—products) and the significant measurement problems due to the extreme conditions in industrial hot forging processes. The first group comprises various measurements of forgings (control during and after forging and mechanical treatment). The second group includes measurements of the forging equipment involving a broadly understood quality control (geometrical characteristics, surface quality etc.), often combined with comprehensive control and durability analysis of forging tools. The work analyzes the possibility and validity of applying scanning techniques for direct control of the quality and change of the geometry of the tools in industrial forging processes, without the necessity of their disassembly, and also measurement systems of temperature, forces and displacement in forging processes.

The article presents the classification of the wear mechanisms of forging tools. The durability of dies can be enhanced through a variety of methods, including the selection of appropriate hot working tool steel, the application of effective heat treatment, the utilization of advanced surface engineering techniques, and the incorporation of lubricating and cooling agents. Two popular methods of tool regeneration, such as re-profiling and laser regeneration, are presented. The issue of numerical wear prediction based on the Archard model, the correlation of this model with experimental results, low-cycle fatigue (HTLCF), and an alternative method based on artificial neural networks are discussed. The paper aims to present currently known wear mechanisms and the methods of increasing and predicting tool durability.