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Research Article | Open Access

Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms

Osman ALTAY1, Turan GURGENC2( ), Mustafa ULAS1, Cihan ÖZEL3
 Software Engineering, Firat University, Elazig 23119, Turkey
 Automotive Engineering, Firat University, Elazig 23119, Turkey
 Mechanical Engineering, Firat University, Elazig 23119, Turkey
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Abstract

In this study, experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with (wt.%) 50FeCrC-20FeW-30FeB and 70FeCrC-30FeB powder mixtures by plasma transfer arc welding were determined. The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory. The linear regression (LR), support vector machine (SVM), and Gaussian process regression (GPR) algorithms are used for predicting wear quantities. A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.

Keywords

wearsurface coatingplasma transfer arc (PTA) weldingpredictionmachine learning algorithms

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Friction
Volume 8 Issue 1,
February 2020
Pages 107-114
DOI: 10.1007/s40544-018-0249-z
Cite this article:
ALTAY O, GURGENC T, ULAS M, et al. Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms. Friction, 2020, 8(1): 107-114. https://doi.org/10.1007/s40544-018-0249-z

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Received: 23 May 2018
Revised: 20 July 2018
Accepted: 22 September 2018
Published: 18 January 2019
© The author(s) 2018

This article is published with open access at Springerlink.com

Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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