Prediction of Elastic Modulus, Yield Strength, and Tensile Strength in Biocompatible Titanium Alloys
Abstract
Titanium alloys are key materials in biomedical engineering, thanks to their exceptional mechanical properties and biocompatibility, which makes them indispensable for medical implants. With the increasing demand for such alloys, the integration of machine learning into their design offers a promising path to reduce costs and speed up the process. This study seeks to develop accurate machine learning models to predict the mechanical properties, including modulus of elasticity, tensile strength, and yield strength, of biocompatible titanium alloys. The results emphasized the significance of including heat treatment parameters and Poisson's ratio, which led to enhanced precision in the prediction of elastic modulus. Moreover, the key roles of iron and tin content in titanium alloys emerged as influential parameters for predicting tensile strength and yield strength, respectively.
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