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Friction 2020, 8(6): 1102-1116 https://doi.org/10.1007/s40544-017-0340-0
Research Article | Open Access | Issue | Published: 12 May 2020

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine

Show Author's Information Mustafa ULAS1,  Osman ALTAY1,  Turan GURGENC2( ),  Cihan ÖZEL3
Department of the Software Engineering, Firat University, Elazig 23119, Turkey
Department of the Automotive Engineering, Firat University, Elazig 23119, Turkey
Department of the Mechanical Engineering, Firat University, Elazig 23119, Turkey
Keywords:
wear loss prediction, surface coating, plasma transferred arc welding, artificial neural network, extreme learning machine
Cite this article:
ULAS M, ALTAY O, GURGENC T, et al. A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine. Friction, 2020, 8(6): 1102-1116. https://doi.org/10.1007/s40544-017-0340-0
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Abstract Full text About this article

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear. Wear tests involve high cost and lengthy experiments, and require special test equipment. The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost, labor, and time. In this study, wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding (PTAW) method with FeCrC, FeW, and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group. The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests. The wear tests were performed at three different loads (19.62, 39.24, and 58.86 N) over a sliding distance of 900 m. In this study, models have been developed by using four different machine learning algorithms (an artificial neural network (ANN), extreme learning machine (ELM), kernel-based extreme learning machine (KELM), and weighted extreme learning machine (WELM)) on the data set obtained from the wear test experiments. The R2 value was calculated as 0.9729 in the model designed with WELM, which obtained the best performance among the models evaluated.


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About this article

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine

Show Author's information Mustafa ULAS1, Osman ALTAY1, Turan GURGENC2( ), Cihan ÖZEL3
Department of the Software Engineering, Firat University, Elazig 23119, Turkey
Department of the Automotive Engineering, Firat University, Elazig 23119, Turkey
Department of the Mechanical Engineering, Firat University, Elazig 23119, Turkey

Abstract

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear. Wear tests involve high cost and lengthy experiments, and require special test equipment. The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost, labor, and time. In this study, wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding (PTAW) method with FeCrC, FeW, and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group. The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests. The wear tests were performed at three different loads (19.62, 39.24, and 58.86 N) over a sliding distance of 900 m. In this study, models have been developed by using four different machine learning algorithms (an artificial neural network (ANN), extreme learning machine (ELM), kernel-based extreme learning machine (KELM), and weighted extreme learning machine (WELM)) on the data set obtained from the wear test experiments. The R2 value was calculated as 0.9729 in the model designed with WELM, which obtained the best performance among the models evaluated.

Keywords: wear loss prediction, surface coating, plasma transferred arc welding, artificial neural network, extreme learning machine

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Received: 28 June 2019
Revised: 01 October 2019
Accepted: 07 November 2019
Published: 12 May 2020
Issue date: December 2020

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