Smart Web Service of Ti-Based Alloy’s Quality Evaluation for Medical Implants Manufacturing

Abstract

The production of biocompatible medical implants is accompanied by technological and time costs. As a result, to be used in the human body, such a product must be of the highest quality. Assessing the quality of biomedical implants made of titanium alloys is relevant given their impact on the health and life of their wearer. In the case of the production of such implants by additive technologies, an important task is to evaluate the properties of the alloys from which it is made. The modern development of Artificial Intelligence allows replacing traditional assessment methods with machine learning methods for such assessment. Existing machine learning methods demonstrate very low classification accuracy, and existing hybrid systems, although increasing classification accuracy, are not sufficient to apply such schemes in practice. The authors improved the hybrid PNN-SVM system to solve this problem in this paper. It is based on the combining use of PNN, Ito Decomposition, and SVM. The PNN’s summation layer outputs were used as additional attributes to an initial dataset. Ito decomposition was used to nonlinearly model relationships between features of an extended dataset. Further classification is carried out using SVM with a linear kernel. The proposed approach’s modeling is performed based on a real-world dataset using the smart web service designed by the authors. Experimentally found an increase in the classification accuracy by 6% of the proposed system compared to existing ones. It makes it possible to use it in practice. Designed smart web service, in which the authors implemented both improved and existing hybrid classification schemes allows to quickly, easily, and without high qualification of the user to implement and explore in more detail chosen classification scheme when classification tasks in various fields of industry.