AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Friction Article
PDF (1.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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

surface coatingplasma transfer arc (PTA) weldingwearpredictionmachine learning algorithms

References

[1]
T Amann, F Gatti, N Oberle, A Kailer, J Rühe. Galvanic ally induced potentials to enable minimal tribochemical wear of stainless steel lubricated with sodium chloride and ionic liquid aqueous solution. Friction 6(2): 230-242 (2018)
Crossref Google Scholar
[2]
J Liu, S Yang, W Xia, X Jiang, C Gui. Microstructure and wear resistance performance of Cu–Ni–Mn alloy based hardfacing coatings reinforced by WC particles. Journal of Alloys and Compounds 654: 63-70 (2016)
Crossref Google Scholar
[3]
M Kommer, T Sube, A Richter, M Fenker, W Schulz, B Hader, J Albrecht. Enhanced wear resistance of molybdenum nitride coatings deposited by high power impulse magnetron sputtering by using micropatterned surfaces. Surface and Coatings Technology 333: 1-12 (2018)
Crossref Google Scholar
[4]
E A G Olivares, V M V Díaz. Study of the hot-wire TIG process with AISI-316L filler material, analysing the effect of magnetic arc blow on the dilution of the weld bead. Welding International 32(2): 139-148 (2018)
Crossref Google Scholar
[5]
R Zahiri, R Sundaramoorthy, P Lysz, and C Subramanian. Hardfacing using ferro-alloy powder mixtures by submerged arc welding. Surface and Coatings Technology 260: 220-229 (2014)
Crossref Google Scholar
[6]
A Motallebzadeh, E Atar, H Cimenoglu. Microstructure and tribological properties of PTA deposited Stellite 12 coating on steel substrate. Manufacturing Science and Technology 3: 224-228 (2015)
Crossref Google Scholar
[7]
H Huang, G Han, Z Qian, Z Liu. Characterizing the magnetic memory signals on the surface of plasma transferred arc cladding coating under fatigue loads. Journal of Magnetism and Magnetic Materials 443: 281-286 (2017)
Crossref Google Scholar
[8]
X Wang, F Han, X Liu, S Qu, Z Zou. Effect of molybdenum on the microstructure and wear resistance of Fe-based hardfacing coatings. Materials Science and Engineering: A 489(1–2): 193-200 (2008)
Crossref Google Scholar
[9]
E Correa, N Alcântara, L Valeriano, N Barbedo, R Chaves. The effect of microstructure on abrasive wear of a Fe–Cr– C–Nb hardfacing alloy deposited by the open arc welding process. Surface and Coatings Technology 276: 479-484 (2015)
Crossref Google Scholar
[10]
M Eroglu. Boride coatings on steel using shielded metal arc welding electrode: Microstructure and hardness. Surface and Coatings Technology 203(16): 2229-2235 (2009)
Crossref Google Scholar
[11]
X Gao, K Dai, Z Wang, T Wang, J He. Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network. Friction 4(2): 105-115 (2016)
Google Scholar
[12]
O Palavar, D Özyürek, A Kalyon. Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy. Materials & Design 82: 164-172 (2015)
Google Scholar
[13]
J C A Batista, C Godoy, A Matthews. Micro-scale abrasive wear testing of duplex and non-duplex (single-layered) PVD (Ti, Al) N, TiN and Cr–N coatings. Tribology International 35(6): 363-372 (2002)
Google Scholar
[14]
F S Lasheras, P G Nieto, F J de Cos Juez, J V Vilán. Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process. Applied Mathematics and Computation 227: 164-170 (2014)
Google Scholar
[15]
G Wang, L Qian, Z Guo. Continuous tool wear prediction based on Gaussian mixture regression model. The International Journal of Advanced Manufacturing Technology 66(9–12): 1921-1929 (2013)
Google Scholar
[16]
L J Xu, J D Xing, S Z Wei, Y Z Zhang, R Long. Artificial neural network prediction on wear properties of high vanadium high speed steel (HVHSS) rolls. Materials Science and Technology 23(3): 315-319 (2007)
Google Scholar
[17]
H Cetinel, H Öztürk, E Celik, B Karlık. Artificial neural network-based prediction technique for wear loss quantities in Mo coatings. Wear 261(10): 1064-1068 (2006)
Google Scholar
[18]
Y F Tan, H Long, X L Wang, H Xiang, W G Wang. Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings. Transactions of Nonferrous Metals Society of China 24(8): 2566-2573 (2014)
Google Scholar
[19]
T Gurgenc, C Ozel. Effect of Heat Input on Microstructure, Friction and Wear Properties of Fe-Cr-B-C Coating on AISI 1020 Surface Coated by PTA Method. Fırat University Turkish Journal of Science & Technology 12(2): 43-52 (2017)
Google Scholar
[20]
C Ozel, T Gurgenc. Effect of heat input on microstructure, wear and friction behavior of (wt.-%) 50FeCrC-20FeW-30FeB coating on AISI 1020 produced by using PTA welding. PloS one 13(1): e0190243 (2018)
Google Scholar
[21]
J Han, J Pei, M Kamber. Data mining: concepts and techniques. Elsevier (2011)
Google Scholar
[22]
C Cortes, V Vapnik. Support-vector networks. Machine Learning 20(3): 273-297 (1995)
Crossref Google Scholar
[23]
R G Brereton, G R Lloyd. Support vector machines for classification and regression. Analyst 135(2): 230-267 (2010)
Crossref Google Scholar
[24]
S R Gunnb. Support vector machines for classification and regression. ISIS Technical Report 14(1): 5-16 (1998)
Google Scholar
[25]
D Kong, Y Chen, N Li. Gaussian process regression for tool wear prediction. Mechanical Systems and Signal Processing 104: 556-574 (2018)
Crossref Google Scholar
[26]
S Aye, P Heyns. An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mechanical Systems and Signal Processing 84: 485-498 (2017)
Crossref Google Scholar
[27]
S Roberts, M Osborne, M Ebden, S Reece, N Gibson, S Aigrain. Gaussian processes for time-series modelling. Phil Trans R Soc A 371(1984): 20110550 (2013)
Crossref Google Scholar
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

790

Views

28

Downloads

78

Crossref

N/A

Web of Science

72

Scopus

0

CSCD

Altmetrics
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.
Return