Development of a machine learning-based surrogate model for friction prediction in textured journal bearings

HIGHLIGHTS

• What: From the literatures, the studies in which ML techniques are applied to the performance prediction of textured journal bearings are still new. This work shows the potential of ML algorithms in efficiently predicting the friction performance of textured journal bearings with varying operating conditions and texture parameters. Ju To verify the accuracy of the present model, the validation is carried out with the same structural parameters and operating conditions as in the experiments.
• Who: Wang and Yujun from the Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Aachen, Germany have published the . . .
  

   

  

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