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

  • Osman Altay
  • Turan GurgencEmail author
  • Mustafa Ulas
  • Cihan Özel
Open Access
Research Article


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.


surface coating plasma transfer arc (PTA) welding wear prediction machine learning algorithms 



All the Matlab scripts of related algorithms in the article are coded ourselves. The used Matlab platform is licensed by Firat University.


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Copyright information

© The author(s) 2018

Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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.

Authors and Affiliations

  • Osman Altay
    • 1
  • Turan Gurgenc
    • 2
    Email author
  • Mustafa Ulas
    • 1
  • Cihan Özel
    • 3
  1. 1.Software EngineeringFirat UniversityElazigTurkey
  2. 2.Automotive EngineeringFirat UniversityElazigTurkey
  3. 3.Mechanical EngineeringFirat UniversityElazigTurkey

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