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Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks

1
Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
2
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121 Athens, Greece
3
Department of Mechanical Engineering, University of West Attica, 12244 Egaleo, Greece
4
Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), ASPETE Campus, N. Heraklion, GR 14121 Athens, Greece
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2788; https://doi.org/10.3390/app9142788
Received: 2 June 2019 / Revised: 5 July 2019 / Accepted: 5 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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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. View Full-Text
Keywords: artificial intelligence techniques; artificial neural networks; soft computing techniques; tribological performance artificial intelligence techniques; artificial neural networks; soft computing techniques; tribological performance
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Cavaleri, L.; Asteris, P.G.; Psyllaki, P.P.; Douvika, M.G.; Skentou, A.D.; Vaxevanidis, N.M. Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks. Appl. Sci. 2019, 9, 2788.

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