A system of analysis and prediction of the loss of forging tool material applying artificial neural networks

Abstract

The article presents the use of artificial neural networks (ANN) to build a system of analysis and forecasting of the durability of forging tools and the process of acquiring the source knowledge necessary for the network learning process. In particular, the study focuses on the prediction of the geometrical loss of the tool material after different surface treatment variants.The methodology of developing neural network models and their quality parameters is also presented. The standard single-layer MLP networks were used here; their quality parameters are at a high level and the results presented with their participation give satisfactory results in line with technological practice. The data used in the learning process come from extensive comprehensive performance tests of forging tools operating under extreme operating conditions (cyclic mechanical and thermal loads). The parameterization of the factors important for the selected forging process was made and a database was developed, including 900 knowledge vectors, each of which provided information on the size of the geometrical loss of the tool material (explained variables). The value of wear was determined for the set values of explanatory variables such as: number of forgings, pressure, temperature on selected tool surfaces, friction path and the variant of the applied surface treatment. The results presented in the study, confirmed by expert technologists, have a clear applicational character, because based on the presented solutions, the optimal treatment can be chosen and the appropriate preventive measures applied, which will extend the service life.

Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel

“…Figure 2 presents a fragment of the developed set, while the details related to the knowledge acquisition are described in ref. [24,25].…”

“…The studies of the system creation process and the results generated therefrom are described in detail in ref. [24,25]. The research described in this work is devoted to the sensitivity analysis of neural networks implemented in the system.…”

“…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. In the literature, one can find many publications describing computer systems developed to support the forging processes [22][23][24][25][26][27][28][29][30][31][32][33][34]. The main goal of all these works is to investigate the financial and ecological aspects of materials production, combined with the scientific and cognitive goals aiming at a continuous improvement of the forging process technology and development of new solutions and technologies [1].In [28], an artificial neural network was developed.…”

“…The best matching and the smallest error were, however, achieved with the use of artificial neural networks. The works [24][25][26] present a methodology of system creation, the parameters and architectures of the developed networks as well as an analysis of the results obtained by means of the elaborated system. This study concerns a sensitivity analysis of artificial neural networks developed for a system predicting the durability of forging tools used in hot die forging processes.…”

Archives of Foundry Engineering

“…This modelling uses the most popular methods in the field of artificial intelligence, namely: • Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) -known as universal approximators of functions, useful mainly when it is necessary to model phenomena of a strongly non-linear nature and multi-dimensional functional relationships, which are difficult to determine in a purely analytical form. These methods perfectly cope with uncertain and incomplete source data, and uncertainty and incompleteness are the predominant features of the source data obtained from experiments [15][16][17]. The source data obtained from experiments is valid only for selected cases, and so it is incomplete, it is additionally burdened with measurement errors, and so it is uncertain.…”