Open Access

ARTICLE

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

Ivan Izonin^1,*, Roman Tkachenko¹, Michal Gregus², Zoia Duriagina^1,3, Nataliya Shakhovska¹

1 Lviv Polytechnic National University, Lviv, 79013, Ukraine
2 Comenius University in Bratislava, Bratislava, 82005, Slovak Republic
3 The John Paul II Catholic University of Lublin, Lublin, 20-708, Poland

* Corresponding Author: Ivan Izonin. Email:

Computers, Materials & Continua 2022, 71(3), 5933-5947. https://doi.org/10.32604/cmc.2022.022582

Received 12 August 2021; Accepted 16 November 2021; Issue published 14 January 2022

Abstract

The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder’s alloys. It provides new possibilities for the efficient production of biocompatible implants for various functional purposes. However, the accuracy of the methods and models used should be as high as possible. In this paper we designed a hybrid PNN-SVM (Probabilistic Neural Network-Support Vector Machine) high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants. We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology. Subsequent classification based on the expanded dataset is performed using SVM. We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr. We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods.

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Citations