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Article

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

Multidisciplinary Analysis, Inverse Design, Robust Optimization and Control (MAIDROC) Laboratory, Department of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USA
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Metals 2021, 11(1), 15; https://doi.org/10.3390/met11010015
Received: 28 November 2020 / Revised: 18 December 2020 / Accepted: 19 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Titanium and Its Alloys for Biomedical Applications)
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. View Full-Text
Keywords: Ti-based biomaterials; biocompatibility; toxicity; β-phase; ω-phase; CALPHAD; artificial intelligence; deep learning artificial neural network (DLANN); self-organizing maps (SOM) Ti-based biomaterials; biocompatibility; toxicity; β-phase; ω-phase; CALPHAD; artificial intelligence; deep learning artificial neural network (DLANN); self-organizing maps (SOM)
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MDPI and ACS Style

Jha, R.; Dulikravich, G.S. Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence. Metals 2021, 11, 15. https://doi.org/10.3390/met11010015

AMA Style

Jha R, Dulikravich GS. Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence. Metals. 2021; 11(1):15. https://doi.org/10.3390/met11010015

Chicago/Turabian Style

Jha, Rajesh, and George S. Dulikravich 2021. "Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence" Metals 11, no. 1: 15. https://doi.org/10.3390/met11010015

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