Analysis and Classification of Temperature Measurements during Melting and Casting of Alloys Using Neural Networks

In this article, we consider the organizational issues of monitoring the thermal conditions of melting and casting alloys at foundries. It is noted that the least reliable method is when the measuring and fixing the temperature are assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, while the values are recorded automatically. This method assumes an algorithm implementation for automatic classification of temperature measurements based on an end-to-end array of data obtained in production series. This task solution is divided into three stages. Preparing of raw data for the classification process is provided in the first stage. In the second stage, the task of measurement classification is solved by using principles of artificial neural networks. Analysis of the artificial neural network results has shown its high efficiency and degree of correspondence with the actual situation at the work site. It is also noted that the application of artificial neural networks principles makes the classification process flexible due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the results. Correctly performed data classification provides an opportunity not only to assess agreements with technological efficiency at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of melting and casting alloys with minimal costs for melting monitoring.