Accelerated Development of Titanium Alloys for Implant Applications

Abstract
In recent years, titanium and its alloys have been widely used as biomaterials for implant applications primarily due their excellent combination of enhanced strength, lower modulus, enhanced corrosion resistance, and good tissue tolerance as compared with stainless steels and cobalt-based alloys. While the use of titanium base alloys had been quite beneficial for orthopaedic implants, it should be noted that most of the alloys that are currently in use, such as Ti-6Al-4V, were originally developed for other applications such as for aircraft engines. This has resulted in a rather limited number of alloys, currently available, which exhibit the optimal combination of biocompatibility and mechanical properties, and have achieved the appropriate degree of maturity.

Proposed Program
The new paradigm in materials design involves an effective and synergistic coupling of computational and experimental techniques. This has led to the establishment of the Center for the Accelerated Maturation of Materials (CAMM) at the Ohio State University (OSU), the aim of which is to propose and effect a new approach to the accelerated maturation and optimization of new and existing materials. This approach is targeted towards the replacement of extensive experimental testing by the development of an integrated set of computational models and tools. Using these computational tools is expected to significantly reduce extensive testing schedules and processing and inspection procedures, and permit development and insertion of new materials within acceptable costs and time frames. To enable these computational tools to be physically relevant, it is necessary that they be integrated with a group of supporting technologies. These technologies involve firstly experimental efforts focused on the provision of accurate physical descriptions of new materials systems and the deformation mechanisms involved in such materials systems to increase the fidelity of predictive models, secondly aspects of combinatorial materials science to populate databases rapidly and efficiently, and finally critical validation of predictions from the computational models. Success in this endeavor requires the intimate coupling of the computational activity with experimental characterization and validation.

The initial focus of CAMM has been on structural materials used primarily in the aerospace industry, such as Ti-alloys and Ni-base superalloys. Consequently, the emphasis has been on the development of computational tools for predicting critical mechanical properties such as yield stress, tensile strength, and fatigue resistance. Since these mechanical properties are equally relevant to biomaterials for implant applications, it has been recognized that the proposed development of such a predictive capability is likely to have a substantial impact on the development of novel biomaterials and the accelerated maturation of existing ones. Furthermore, the first candidate alloy being studied in CAMM is Ti-6Al-4V, which as discussed earlier, is currently one of the most widely used implant alloys.

The proposed approach would have primary objectives. Firstly, to develop a capability for predicting yield strength, elongation, and modulus for a set of candidate Ti-base alloys for implant applications. Secondly, to use a combinatorial approach for rapidly assessing the mechanical properties of potential alloy compositions for implant applications. It is expected the both research activities directed towards both objectives would occur in a concurrent fashion. For the first objective the selection of candidate alloys would include both well-matured alloys, such as Ti-6Al-4V, as well as more recent compositions based on Ti-Mo-Zr-Fe (TMZF™) and Ti-Nb-Zr-Ta (TNZT) systems, which have achieved a lower degree of maturity. Since, microstructure plays a key role in determining the mechanical properties in these titanium alloys, an important thrust of the proposed program would be to include microstructural details in the development of computational tools. Initially a rules-based approach will be adopted for predicting relevant mechanical properties such as modulus and yield strength. The rules-based approach will involve the development of a neural-network employing an extensive microstructurally-based database. The database will consist of quantified microstructural features and corresponding yield strength and modulus values. Microstructural variations will be affected in these alloys by systematic heat-treatments. These heat-treatments will be carried out in a Electro-Thermal Mechanical Testing (ETMT™) system. The ETMT™ system, a recent development from the National Physical Laboratory in the U.K. and marketed by Instron, is ideally suited for thermo-mechanical treatments of miniature specimens as well as for measuring their mechanical properties. CAMM is currently in the process of acquiring an ETMT™ system from Instron. Using such miniature test specimens would allow for rapid assessment of mechanical properties for an extensive set of heat-treatments from a limited stock of material These heat-treatments are expected to influence a number of microstructural features such as, prior b grain size, size distribution and morphology of a precipitates, and volume fraction of a. The characterization of these microstructural features will be carried out using optical metallography, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Quantification of these features is a rather challenging endeavor due to the complexity of the microstructures involved and their three dimensional nature. These microstructural features will quantified using a set of rigorous procedures based on stereology. Such procedures have been developed for a/b Ti alloys, such as Ti-6Al-4V, by CAMM researchers using financial support from the Metals Affordability Initiative of the Air Force Research Laboratory. Additional stereological procedures, more relevant for metastable b or near-b Ti alloys will be developed as part of the proposed program. The mechanical properties of interest, yield strength, elongation, and modulus, will also be measured by tensile testing of heat-treated miniature specimens in the ETMT™ system. Validation of the tensile properties measured using the ETMT™ system will also be carried out by testing some randomly chosen heat-treated samples using more conventional ASTM standard testing procedures with larger size specimens. The databases developed using these stereological quantification procedures for microstructural features and mechanical testing will form the basis of developing neural-networks for predicting yield strength, elongation, and modulus in these alloys. The first attempt at a neural-network with a limited dataset will involve using a fuzzy logic approach. Such a fuzzy logic based neural network has been successfully developed in CAMM for predicting yield strength of Ti-6Al-4V as a function of relevant microstructural features. Fuzzy logic models will be developed for predicting yield strength, elongation, and modulus for the candidate alloys in the Ti-Mo-Zr-Fe and Ti-Nb-Zr-Ta systems as part of the proposed program. While fuzzy logic models can make reasonable predictions, they are unable to estimate the errors associated with the predictions. Therefore, the second step in the development of the neural networks will involve the use of Bayesian statistics. Neural-networks based on Bayesian statistics are mathematically more robust and capable of estimating the errors associated the predictions. However, Bayesian neural-networks require extensive databases and will therefore be attempted only when a reasonably large database has been developed for the alloys of interest.

The second objective of the proposed program is to use a combinatorial approach for the development of novel implant Ti-alloys. This approach will involve the use of the Laser Engineered Net Shaping (LENS™) facility located in CAMM at the Ohio State University. The LENS™ process uses a focused laser beam as a heat source to melt metallic powders and create a solid, three-dimensional object. Traditionally, the alloys deposited using this process have primarily been from pre-alloyed powders of the required composition. However, since the LENS™ process uses a powder feedstock, it allows the flexibility to deposit a blend of elemental powders and create an alloy in situ. We have pioneered the use of a blend of elemental powders to create an in situ alloy in LENS™ deposits. Furthermore, using the dual-powder feeder capability in our LENS™ unit we have successfully deposited compositionally graded Ti-base alloys achieving variations ~ 25 wt% within a distance of ~ 25 mm. Such compositionally graded Ti-base alloys, deposited from a blend of elemental powders, will be used for rapid assessment of mechanical properties of potentially useful implant alloy compositions. Thus, a series of different graded Ti-base alloys, with compositions centered around the candidate alloys identified for study as part of the first objective, will be deposited using LENS™. The compositional profile in these graded alloys will be engineered in the form of steps, such that subsequent to deposition these alloys can be sectioned into slabs of uniform composition for heat-treatments and mechanical testing. Initial screening of mechanical properties of these graded alloys will also be carried out using micro / nano indentation testing. Nanoindentation experiments will be carried out using the Nanoindenter XP instrument available within CAMM at Ohio State. In addition to mechanical properties, detailed microstructural characterization and quantification of the graded alloys will be carried out using SEM/TEM and the stereological procedures described above. The results of this combinatorial study will subsequently be incorporated into a database consisting of composition variables in addition to microstructural variables together with the attendant mechanical property data. These new databases will be used for the development of a fuzzy logic models for predicting yield strength, and modulus of these alloys as a function of both composition and microstructure. Such models will subsequently be used for isolating the influence of individual composition variables on the mechanical properties and consequently aid in optimizing the composition of existing implant alloys as well in the development of new ones.