An analyze and prediction system the loss of material of forging tools using artificial neural networks (Journal of Mining and Metallurgy, Section B: Metallurgy, 2018)
The article presents the use of artificial neural networks (ANN), to build a system of analysis and forecasting the durability of forging tools and the process of acquiring the source knowledge necessary for the network learning process. In particular, the prediction of the geometrical loss of 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, whose 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 tehermal loads). The parameterization of 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 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 at work confirmed by expert technologists have clearly an application character, because based on the presented solutions, you can choose the optimal treatment and apply appropriate preventive measures that will extend the service life.