Abstract
Current study results on artificial in telligence have been published. According to news reporting out of Pune, India, by NewsRx editors, research stated, "Wear is induced when two surfaces are in re lative motion." Funders for this research include University of Debrecen Program For Scientific Publication. The news reporters obtained a quote from the research from Vishwakarma Institute of Information Technology: "The wear phenomenon is mostly data-driven and affec ted by various parameters such as load, sliding velocity, sliding distance, inte rface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The mach ine learning (ML) approach has not only made it possible to establish the relati on between the operating parameters and wear but also helps in predicting the be havior of the material in polymer tribological applications. In this study, an a ttempt is made to apply different machine learning algorithms to the experimenta l data for the prediction of the specific wear rate of glass-filled PTFE (Polyte trafluoroethylene) composite. Orthogonal array L25 is used for experimentation f or evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distan ce. The experimental data are analysed using ML algorithms such as linear regres sion (LR), gradient boosting (GB), and random forest (RF)."
NETL
NSTL