Advances in improving tribological performance of titanium alloys and titanium matrix composites for biomedical applications: a critical review

Abstract

Titanium (Ti) alloys have been widely used in biomedical applications due to their superior mechanical, physical, and surface properties, while improving their tribological properties is critical to widening their biomedical applications in the current era. The present review examines the recent progress made in enhancing the tribological performance of titanium alloys and titanium matrix composites for biomedical purposes. It specifically focuses on the progress made in biomedical coatings, mechanical surface treatment, and developing titanium matrix composites in terms of their processing, tribological testing conditions, and characterization. Despite thorough investigations, the specific testing procedures for evaluating the friction and wear properties of the alloy and/or biomedical component are still uncertain. The majority of researchers have selected test methods and parameters based on previous studies or their own knowledge, but there is a scarcity of studies that incorporate limb-specific tribological tests that consider the distinct kinematic and biological structure of human limbs. Since advanced microscopy has great potential in this field, a variety of advanced characterization techniques have been used to reveal the relationship between microstructural and tribological properties. Many coating-based strategies have been developed using anodizing, PEO, VD, PVD, nitriding, thermal spray, sol-gel, and laser cladding, however; composition and processing parameters are crucial to improving tribological behaviour. Reinforcing component type, amount, and distribution has dominated Ti matrix composite research. Ti grade 2 and Ti6Al4V alloy has been the most widely used matrix, while various reinforcements, including TiC, Al2O3, TiB, hydroxyapatite, Si3N4, NbC, ZrO2 have been incorporated to enhance tribological performance of Ti matrix. Mechanical surface treatments improve biomedical Ti alloys' tribological performance, which is advantageous due to their ease of application. The implementation of machine learning methods, such as artificial neural networks, regression, and fuzzy logic, is anticipated to make a substantial contribution to the field due to their ability to provide cost-effective and accurate results. The microstructural and surface features of biomedical Ti alloys directly affect their tribological properties, so image processing strategies using deep learning can help researchers optimize these properties for optimal performance.