Development of machine learning method of titanium alloy properties identification in additive technologies

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.134319

Keywords:

titanium alloy powders, microstructure, morphology, granulometric composition, additive technologies, artificial intelligence methods

Abstract

Based on the experimentally established data on the parameters of microstructure, elemental and fractional composition of titanium alloy powders, four classes of their conformity (a material with excellent properties, optimal properties, possible defects in the material and defective material) as source raw materials for the additive technologies are identified. The basic characteristics of the material, which determine its belonging to a certain class, are established. Training and test samples based on 20 features that characterize each of the four classes of titanium alloy powders for the implementation of machine learning procedures were built. The developed method for identification of the class of material, based on the use of the second-order Kolmogorov-Gabor polynomial and the Random Forest algorithm, is described. An experimental comparison of the developed method work results with existing methods: Random Forest, Logistic Regression, and Support Vectors Machines based on the accuracy of their work in the training and application modes was made. The visualization of the results of all the investigated methods was given.

The developed supervised learning method allows constructing models for processing a large number of each input vector characteristics. In this case, the Random Forest algorithm provides satisfactory generalization properties while retaining the advantages of an additional increase of the accuracy based on the Kolmogorov-Gabor polynomial.

The main advantages of the developed method, in particular, regarding the additional increase of the accuracy of the classification task solution, are experimentally determined. The developed method allows increasing the modeling accuracy by 34.38, 33.34 and 3.13 % compared with the methods: Support Vectors Machine, Logistic Regression, and Random Forest respectively.

The obtained results allow one to considerably reduce financial and time expenses during the manufacture of products by additive technologies methods. The use of artificial intelligence tools can reduce the complexity and energy efficiency of experiments to determine the optimum characteristics of powder materials.

Author Biographies

Roman Tkachenko, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

Doctor of Technical Sciences, Professor, Head of Department

Department of Publishing Information Technologies

Zoia Duriagina, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013 Katolicki Uniwersytet Lubelski Jana Pawła II Al. Racławickie, 14, Lublin, Poland, 20-950

Doctor of Technical Sciences, Professor

Department of Applied Materials Science and Materials Engineering

Ihor Lemishka, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

Postgraduate student

Department of Applied Material Science and Materials Engineering

Ivan Izonin, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

PhD, Assistant

Department of Publishing Information Technologies

Andriy Trostianchyn, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

PhD, Assistant

Department of Applied Materials Science and Materials Engineering

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Published

2018-06-20

How to Cite

Tkachenko, R., Duriagina, Z., Lemishka, I., Izonin, I., & Trostianchyn, A. (2018). Development of machine learning method of titanium alloy properties identification in additive technologies. Eastern-European Journal of Enterprise Technologies, 3(12 (93), 23–31. https://doi.org/10.15587/1729-4061.2018.134319

Issue

Vol. 3 No. 12 (93) (2018): Materials Science

Section

Materials Science

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Copyright (c) 2018 Roman Tkachenko, Zoia Duriagina, Ihor Lemishka, Ivan Izonin, Andriy Trostianchyn

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