Abstract
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.
Highlights
In forging companies producing hot die forgings, the durability of forging tools is an important and at the same time complex scientific and economic issue
In the case of the system described in this paper, the formal method of knowledge representation was based on artificial neural networks, which were developed with the use of a set of training data collected in industrial research
The sensitivity analysis was carried out to identify the most important input parameters, which made it possible to rank them in the order from the most significant to the least significant for the results obtained
Summary
In forging companies producing hot die forgings, the durability of forging tools is an important and at the same time complex scientific and economic issue. A development of the IT field can be noticed both in the aspect of the increased possibilities of measurement and storage of a very big number of technological parameters and with respect to providing many new methods and algorithms for the processing of the latter [22] This makes it possible to construct computer systems which enable a partial replacement of the costly and time-consuming material experiments performed by way of computer simulations. The investigations carried out by the authors, both in the area of analysing the forging tool durability, including manyyear studies of industrial forging processes, and the long-term research related to the analysis of the formal methods of creating computer systems supporting these processes, have led to the development of an expert system which predicts the durability of forging tools and makes it possible to calculate the value of the geometrical loss of the analysed tool for the predetermined parameter values of its work. The output (explained) variables included the geometric loss of the tool material and the percentage contribution of the four main destructive mechanisms
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