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LIBORIO CAVALERI

Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks

  • Autori: Cavaleri L.; Asteris P.G.; Psyllaki P.P.; Douvika M.G.; Skentou A.D.; Vaxevanidis N.M.
  • Anno di pubblicazione: 2019
  • Tipologia: Articolo in rivista
  • OA Link: http://hdl.handle.net/10447/387705

Abstract

The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels.