.... This is typically obtained experimentally in a flat plate tester with flow between a smooth and a roughened flat plate. This article presents an alternative approach to training an artificial neural network (ANN) in conjunction with computational fluid dynamics (CFD) modeling to predict friction factors and leakage...

... descent learning algorithm-based artificial neural network (GD-ANN) with optimally tuned network architecture predicted (R 2 ∼ 97%) both the tribological performance attributes (coefficient of friction and specific wear-rate) of the natural silicate-filled friction composites more accurately as compared...

.... Thus, either prediction accuracy or model interpretability suffers when AM and ML models are implemented independently. This study proposes a hybrid model framework to incorporate the benefits of AM and ML simultaneously. In the hybrid model, an artificial neural network (ANN) compensates the AM...

... strength), processing procedure, heat treatment and tribological test variables (normal load, sliding speed, and sliding distance) on tribological properties and established two-parameter relationships. These data are analyzed using several ML algorithms: artificial neural network (ANN), K nearest neighbor...

... Division of ASME for publication in the J ournal of T ribology . 10 06 2020 12 11 2020 13 11 2020 08 01 2021 fractal theory fretting wear artificial neural network rough surface abrasion adhesion contact mechanics dry friction surface fatigue and fretting Based...

...Haibo Xie; Zhanjiang Wang; Na Qin; Wenhao Du; Linmao Qian An integrated finite element and artificial neural network method is used to analyze the impact of scratch process parameters on some variables related to elastoplastic deformation of titanium alloy. The elastoplastic constitutive parameters...