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

Cavaleri, L; Asteris, PG; Psyllaki, PP; Douvika, MG; Skentou, AD; Vaxevanidis, NM

doi:10.3390/app9142788

Archivio istituzionale della ricerca dell'Università degli Studi di Palermo

IRIS è il sistema di gestione integrata dei dati della ricerca (persone, pubblicazioni, progetti, attività) adottato dall’Università di Palermo, e ha lo scopo di raccogliere, conservare, documentare, e diffondere ad accesso aperto le informazioni relative alla produzione scientifica degli autori afferenti all’Ateneo.

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.

Data di pubblicazione:	2019
Titolo:	Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks
Autori:	CAVALERI, Liborio [Membro del Collaboration Group] Asteris P. G. [Membro del Collaboration Group] Psyllaki P. P. [Membro del Collaboration Group] Douvika M. G. [Membro del Collaboration Group] Skentou A. D. [Membro del Collaboration Group] Vaxevanidis N. M. [Membro del Collaboration Group] mostra contributor esterni nascondi contributor esterni
Citazione:	Cavaleri, L., Asteris, P., Psyllaki, P., Douvika, M., Skentou, A., & Vaxevanidis, N. (2019). Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. APPLIED SCIENCES, 9(14).
Rivista:	APPLIED SCIENCES
Digital Object Identifier (DOI):	10.3390/app9142788
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.
URL:	https://res.mdpi.com/applsci/applsci-09-02788/article_deploy/applsci-09-02788.pdf?filename=&attachment=1
Appare nelle tipologie:	1.01 Articolo in rivista

File in questo prodotto:

File	Descrizione	Tipologia	Licenza
cavaleri2019 asteris.pdf		Versione Editoriale		Open Access Visualizza/Apri

Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/10447/387705

Citazioni

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.