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.

Cavaleri L., Asteris P.G., Psyllaki P.P., Douvika M.G., Skentou A.D., Vaxevanidis N.M. (2019). Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. APPLIED SCIENCES, 9(14) [10.3390/app9142788].

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

Cavaleri L.
Membro del Collaboration Group
;
2019-01-01

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.
https://res.mdpi.com/applsci/applsci-09-02788/article_deploy/applsci-09-02788.pdf?filename=&attachment=1
Cavaleri L., Asteris P.G., Psyllaki P.P., Douvika M.G., Skentou A.D., Vaxevanidis N.M. (2019). Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. APPLIED SCIENCES, 9(14) [10.3390/app9142788].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/387705
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