Abstract
The tribological properties of matching pairs at different temperature domain is predicted through friction sound. Two deep learning algorithms respectively by virtue of the Nonlinear Auto-Regressive models with Exogenous Inputs and long short-term memory neural network are adopted to analyze the acoustic features of friction sound to predict tribological properties of polymer surface at five temperatures within a large temperature domain under different working conditions. The deep learning algorithm precisely fits the friction coefficient in accordance with the performance analysis.
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