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. Keyword: hybrid machine learning, statistical pattern recognition, network theory, fractals Pove~anje obrabne odpornosti litega`eleza po laserski obdelavi je posledica ne le ustrezne strukturne in fazne sestave, temvet udi izbolj{anja pogojev trenja zaradi grafita v obmo~ju laserja. Lasersko kaljenje pove~a tudi odpornost proti obrabi jekel in nekaterih drugih zlitin s stali{~a trenja v kislem in alkalnih okoljih. Avtorji predstavljajo novo metodo hibridnega sistema strojnega u~enja z uporabo nove metode za prepoznavanje statisti~nih vzorcev s teorijo omre`ja pri robotnem laserskem kaljenju (RLH). Zdru`ili so metodo multiple regresije, metode podpornih vektorjev in metodo nevronskih mre`. Za prepoznavanje statisti~nih vzorcev so uporabili topolo{ke lastnosti teorije omre`ja. Enakomerna porazdelitev topolo{kih lastnih 16-300 triad v razli~nih ravneh organizacije in mre`e v mikrostrukturi RLH ka`e, da obstaja mo~na povezava preko omre`ja in aktivna povezava med iglicami martenzita. Klju~ne besede: hibridno strojno u~enje, statisti~no razpoznavanje vzorcev, teorija omre`ij, fraktali

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

“…utilize when structuring data on hyperplanes. Previous studies [1,6] suggested that the RBF function worked better for tribological data. Two variables, the kernel coefficient (γ) and the regularization factor (C), determine how well an SVM model performs.…”

“…Because of its atomically flat surfaces and ultrathin layers, graphene has applications at both the nano and microscales. Graphene lasts a long time since it has a high mechanical strength [6]. Using the nanoindentation method of atomic force microscopy, Arun et al [7] determined that graphene is the most robust material yet quantified for monolayer graphene membranes.…”