Abstract
In this work, we studied a Ti-Nb-Zr-Sn system for exploring novel composition and temperatures that will be helpful in maximizing the stability of β phase while minimizing the formation of α” and ω-phase. The Ti-Nb-Zr-Sn system is free of toxic elements. This system was studied under the framework of CALculation of PHAse Diagram (CALPHAD) approach for determining the stability of various phases. These data were analyzed through artificial intelligence (AI) algorithms. Deep learning artificial neural network (DLANN) models were developed for various phases as a function of alloy composition and temperature. Software was written in Python programming language and DLANN models were developed utilizing TensorFlow/Keras libraries. DLANN models were used to predict various phases for new compositions and temperatures and provided a more complete dataset. This dataset was further analyzed through the concept of self-organizing maps (SOM) for determining correlations between phase stability of various phases, chemical composition, and temperature. Through this study, we determined candidate alloy compositions and temperatures that will be helpful in avoiding/minimizing formation of α” and ω-phase in a Ti-Zr-Nb-Sn system. This approach can be utilized in other systems such as ω-free shape memory alloys. DLANN models can even be used on a common Android mobile phone.
Highlights
Titanium-based alloys have been widely accepted for biomedical applications due to comparatively superior biocompatibility and anti-corrosion properties [1,2,3,4,5]
We used statistical terms for guidance, while we gave priority to physical metallurgy of titanium alloys for deep learning artificial neural network (DLANN) model selection. These DLANN models can be used on a personal computer and even on an Android mobile phone to predict various metastable and stable phases, and provide us with a dataset with additional support points for determining various patterns and correlations within this dataset
artificial intelligence (AI)-based predictions can be performed on a normal computer for free as we have developed our code in Python language, which isalloys free. predicted through CALculation of PHAse Diagram (CALPHAD), Table 4
Summary
Titanium-based alloys have been widely accepted for biomedical applications due to comparatively superior biocompatibility and anti-corrosion properties [1,2,3,4,5]. Young’s modulus (YM) of common implant materials varies between 100–230 GPa, which is significantly higher when compared with that of bone, which is between 10 and 40 GPa [1]. This difference in YM results in non-uniform distribution of stress in the implant materials and the surrounding bone structure. This can result in the failure of an implant [1]. Titanium alloys containing β-phase as the predominant phase are known to have lower values of
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