A novel method for statistical pattern recognition using the network theory and a new hybrid system of machine learning

Abstract

The increase in wear resistance of cast irons after laser treatment is due not only to the corresponding structural and phase composition, but also to the improvement in the friction conditions due to the graphite retained in the laser impact zone. Also, laser hardening increases the wear resistance of steels and some other alloys in terms of the friction in alkaline and acidic environments. In this article we present a new method for a hybrid system of machine learning using a new method for statistical pattern recognition through network theory in robot laser hardening (RLH). We combined the method of multiple regression, the method of a support vector machine and the method of a neural network. For statistical pattern recognition we use the topological properties of network theory. The even distribution of the topological property16-300 triads throughout the various levels of the organization and network in the microstructure of RLH indicates that there is a strong linkage across the network and an active connection among the needles of martensite.