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- W2044833165 abstract "The article presents a newly developed system for on-line estimation of the roughness and hardness parameters of surfaces involved in sliding friction process. The information about the process is extracted from the acoustic emission signal generated by the friction. The detected signal is first transformed into the power spectrum and then mapped into the process parameters by an artificial neural network simulated on a PC. The system performance was tested in the on-line pin-in-disk experiment for four different classes of the surface roughness and/or pin hardness. The recognition reliability for placing given cases in correct classes was found to be greater than 94% for the majority of experiments." @default.
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- W2044833165 date "1995-08-01" @default.
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- W2044833165 title "Application of a neural network to the estimation of surface roughness from ae signals generated by friction process" @default.
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- W2044833165 doi "https://doi.org/10.1016/0890-6955(95)90402-8" @default.
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