Abstract
Fresh data on artificial intelligence are presented in a new report. According to news originating from Shanghai, People’s Republic of China, by NewsRx editors, the research stated, “In this work, ball-ondisk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness.” Financial supporters for this research include National Natural Science Foundation of China; Industrial Collaborative Innovation Project of Shanghai; Leading Talents Program of Shanghai; Natural Science Foundation Project of Shanghai; Foundation of Science And Technology Commission of Shanghai Municipality; Guangdong Basic And Applied Basic Research Foundation; Project of Department of Education of Guangdong Province. The news reporters obtained a quote from the research from Shanghai Institute of Technology: “In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, namely Random Forest (RF), K-neighborhood (KNN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were applied to predict wear depth. By analyzing the performance of several ML algorithms, it is demonstrated that ball bearing wear depth can be estimated by ML models by inputting different parameter variables.”
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