Artificial neural network-based prediction of tribological performance of coatings fabricated by thermal spray techniques for nuclear energy applications

Document Type

Article

Publication Title

Franklin Open

Abstract

Nuclear energy systems are subject to harsh operating conditions, such as wide temperature variations and humidity changes with altitude shifts, which can lead to wear in different devices. The coatings of single or multi-layer are excellent solution for control over the wear rate. The beneficial characteristics of these multilayer coatings may be expertly customised for any application using artificial neural network (ANN) based models. Accordingly present work is for the rate of sliding wear and friction of hard coatings sprayed by two various types of thermal spray processes is predicted using a model created using an artificial neural network approach. The mechanical and tribological performances of four types of deposits, including coatings made of WC20Cr3C27Ni and NiCrSiBFe that were deposited using APS and HVOF, were examined and analysed. The controllable factors considered in the developed ANN model are sliding distance, load, velocity, temperature of tribo-chamber, standoff distance, gun transverse speed, powder feed rate, porosity, and nanohardness. The ANN made it feasible to study the interactions between the two output parameters and the nine input parameters. The optimal outcome of deposited film characteristics like friction and wear are evaluated using regression and performance curve analysis. The studied data demonstrates an excellent consistency between the experimental and predicted tribological parameters. The evaluation of the mean impact value was employed to quantitatively ascertain the significance of each input parameter, with the objective of enhancing prediction accuracy.

DOI

10.1016/j.fraope.2025.100364

Publication Date

9-1-2025

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