Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence

Abstract

This paper presents an artificial intelligence (AI) framework proposed to predict the optimum composition of the NiTi shape memory alloy (SMA) to be used in dental applications. A multilayer feed forward neural network (MLFFNN) was adopted for machine learning (ML) model to train the readily available experimental data in literature on the Ni ion release from a variety of NiTi compositions into artificial saliva (AS) solutions to predict the NiTi SMA composition to exhibit the lowest amount of Ni ion release into oral cavity. As a result, 51.5 at.% Ni - balance Ti composition was predicted to be the optimum NiTi SMA composition to release the lowest amount of Ni ions into the oral cavity, which was supported by the validation experiments utilizing static immersion experiments carried out in AS and the post-mortem inductively coupled plasma mass spectrometer (ICP-MS) analysis of the immersion fluids. The findings of the work presented herein not only demonstrate that the proposed AI framework successfully predicts the most biocompatible NiTi SMA for dental applications, but also open a venue for the utility of the current AI framework in the design of other medical alloys and SMAs for a variety of applications.