Development of machine learning method of titanium alloy properties identification in additive technologies
Journal Title: Восточно-Европейский журнал передовых технологий - Year 2018, Vol 3, Issue 12
Abstract
<p>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.</p><p>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.</p><p>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.</p><p>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.<strong></strong></p>
Authors and Affiliations
Roman Tkachenko, Zoia Duriagina, Ihor Lemishka, Ivan Izonin, Andriy Trostianchyn
Keywords
Related Articles
- EP ID EP528039
- DOI 10.15587/1729-4061.2018.134319
- Views 54
- Downloads 0
Principles of construction and identification of a multilevel system for monitoring parameters of technological cycle of casting
<p>For the creation of a multi-level system of integrated control and operational management of physical-chemical and technological casting processes, deterministic causality of technological objects was determined. Thes...
Determining the composition of burned gas using the method of constraints as a problem of model interpretation
<p>This paper proposes a method for solving the problem on determining the unknown composition of a gaseous hydrocarbon fuel during its combustion in real time. The problem had been defined as the inverse, ill-posed prob...
Assessment of capabilities of military groupings (forces) based on the functional group “Engage”
<p class="a"><span lang="EN-US">An approach to assessing the capabilities of groupings of troops (force</span><span lang="EN-US">s) based on the use of the combinatorial method with the limited base of arguments on the e...
Selection of new working fluids for a heat-using compression refrigerating machine with the block «turbine- compressor»
<p>The compression heat-using refrigerating machines operating in the Chistiakov-Plotnikov cycle use recycled waste heat of power machines as primary energy for producing cold of various temperature potentials thus savin...
Hydrothermal mode of the flowthrough reservoircooler with respect to its morphometric changes
<p>We carried out hydrothermal investigations of the flow-through reservoir-cooler with running water of the thermal power plant. The purpose of the study was to investigate a degree of silting of the flow-through reserv...