MODELING AND INVESTIGATION OF THE WEAR RESISTANCE OF SALT BATH NITRIDED AISI 4140 VIA ANN

Şerafettin Ekinci (), Ahmet Akdemir () and Humar Kahramanli ()
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Şerafettin Ekinci: Department of Mechanical Engineering, Technology Faculty, Selçuk University, Alaeddin Keykubat Campus, Selcuklu Konya 42003, Turkey
Ahmet Akdemir: Department of Mechanical Engineering, Faculty of Engineering and Architecture, Selçuk University, Alaeddin Keykubat Campus, Selcuklu, Konya 42003, Turkey
Humar Kahramanli: Department of Computer Engineering, Technology Faculty, Selçuk University, Alaeddin Keykubat Campus Selcuklu, Konya 42003, Turkey

Surface Review and Letters (SRL), 2013, vol. 20, issue 03n04, 1-10

Abstract: Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests were performed on a block-on-cylinder machine in which carbonized AISI 52100 steel discs were used as the counter face. For the examined AISI 4140 steel samples with and without surface treatment, the evolution of both the friction coefficient and of the wear behavior were determined under various loads, at different sliding velocities and a total sliding distance of 1000 m. The test results showed that wear resistance increased with the nitriding process, friction coefficient decreased due to the sulfur in salt bath and friction coefficient depended systematically on surface hardness. For the second part of this study, four artificial neural network (ANN) models were designed to predict the weight loss and friction coefficient of the nitrided and unnitrided AISI 4140 steel. Load, velocity and sliding distance were used as input. Back-propagation algorithm was chosen for training the ANN. Statistical measurements ofR2, MAE and RMSE were employed to evaluate the success of the systems. The results showed that all the systems produced successful results.

Keywords: AISI 4140 steel; ANN; backpropagation; dry friction; wear; modeling; salt bath nitriding; tribology (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0218625X13500339

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