In this paper, we present an application of artificial neural network (ANN) analysis in the thermovision identification of the studied thermal fields. Precise thermal field identification plays an important role in distinguished technological processes, for instance in metallurgy. Our efforts were focused in this direction. Thermovision outputs are usually thermograms with a form of a quasi-coloured imaging record of an observed temperature field. A thermogram is usually registered and presented in a form of an electronic or printed image. The character of such a document is informational only, and real temperature values are difficult to detect. The exploitation of neural networks is advantageous, if it is necessary to express complex mutual relations among sensor-based data. More accurate results of the predictions of different metallurgical parameters with the exploitation of neural networks are based on the fact that the application of neural networks enables the assignment of relations among process parameters which cannot be traced using common methods due to their mutual interactions, the considerable amount of data, dynamics and the thus ensuing time demands.
Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .