A method for automated classification of steel microstructures based on extraction of informative parameters and neural network implementation

• Authors: Irina Topalova;Alexander Tzokev;Anton Mihailov
• Venue: AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
• Year: 2009

During the in-service process the structural composition of the steel is changed bringing different damages which can lead to complete breakdown. It is appropriate to develop methods for automated classification of steel microstructures aiming high recognition accuracy, reliability and lack of any subjective evaluation. The common problem in all of the existing methods for texture classification is the low achieved recognition accuracy rate. That is the reason for searching for new reliable methods giving higher classification accuracy. The goal of the represented research is to propose a new method for automated classification of heat resistant steel structures aiming higher accuracy and computational simplicity in comparison to other existing methods. The proposed method is based on recognition of microscope images for representative steel structures having different aging stage grouped in five classes. In the preprocessing stage the histograms of the images are extracted, stretched and a method for choosing a set of the more informative values of the histogram cover curve is developed. The reduced number of values are given to the input layer neurons of a MLP type neural network. The achieved 100% accuracy and computational simplicity 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 systems.