Automated classification of heat resistant steel structures based on neural networks

Gathering enough reliable information for the moment state of metal structures is needed to measure the rest life time and to assure usage without failures for devices in thermo-electric power plants. This information can be obtained by microstructure analysis of metal specimens by using plastic replicas and structural analysis. During the in-service process (high pressure and temperature) the structural and the phase composition of the steel are changed bringing different damages which can lead to complete breakdown. It is appropriate to develop methods for automated classification of steel structures aiming high recognition accuracy, reliability and lack of any subjective evaluation. The goal of the represented research is to propose a new method for automated classification of heat resistant steel structures aiming higher accuracy in comparison to other existing methods. The proposed method is based on recognition of microscope images for representative steel structures having different aging stage. In the preprocessing stage the histograms of the images are extracted and a set of reduced numbers of the cover curve values are given to the input neurons of a MLP type neural network. The achieved 100% accuracy is a good preposition to implement the method for automated calculation of the remaining capacity of the steel avoiding the subjective evaluation factor and implementing it in a real time working system.