Titanium-Based Alloys with High-Performance: Design and Development

Abstract

In recent years, titanium alloys with better properties have become increasingly popular. Their composition must be precisely designed to meet these demands. Screening alloy properties such as corrosion resistance, specific strength, properties to service at high temperatures, and microstructural stability requires a fair amount of effort and money to accomplish. By taking titanium-based alloys as an example, this chapter reviews the use of high-performance alloy design and development approach for industrial applications, in order to simplify the selection of titanium alloy compositions. The different high-throughput alloy design methods have been used by researchers to calculate diffusion coefficients of multiple elements using a thermodynamic database of atomic mobility. A composition with comprehensively optimal properties is selected by applying a rigorous screening criterion and then evaluating it in an experimental setting in order to come up with an optimal composition. Comparing this strategy with the data-driven material design methods that have been developed in recent times, few methods are more accurate and efficient, mainly because the diffusion pairs, the atomic mobility databases, and the refined physical models work together to make this strategy the most accurate and efficient. This approach could help develop high-performance titanium alloys, to overcome challenges of developing titanium alloys.

1. Introduction

Titanium alloys are used extensively for the manufacture of components used in automobile, chemical, aerospace, power generation, and biomedical applications that are subjected to complex operating conditions. It remains a challenge, however, to develop an alloy that has the desired combination of properties at an affordable cost. As an engineering material, they are useful in the manufacturing of turbine engines and aircraft components. The reason for this is that titanium alloys, among others, exhibit excellent strength at high-temperature applications, better creep resistance properties, good high-temperature microstructural stability, and resistance to corrosion and oxidation. The use of titanium alloys is not suitable for all parts of turbine engines due to phase equilibria and microstructural stability and these conventional titanium alloys cannot withstand operating temperatures greater than 600°C [1].

There is a range of advanced titanium alloys, which may provide titanium alloys with a higher temperature capability, such as TiAl, Ti3Al, and Ti2AlNb, as well as titanium/titanium aluminides [2]. This is a vital issue in terms of ensuring that these engineering materials maintain a high-temperature structural capability in order to be able to meet the increasing requirements for high thrust-to-weight ratios and energy efficiency that are currently being developed for turbine engine applications. These materials must be able to meet the increasing needs of high thrust-to-weight ratios and energy efficiency [3].

Titanium alloys have become more popular over the past few decades due to their ability to alloy with a wide variety of elements, such as Mo, Al, Ta, Zr, V, Zr, Mn, Fe, Ni, Co, Cr, Cu, and Nb [4]. There is no doubt that alloying elements play a crucial role in stabilizing either the low-temperature or high-temperature phases in titanium alloys [3, 5]. Depending upon the alloying elements in varying proportions in an alloy, they often result in the formation of low-temperature and high-temperature phases in the titanium alloy system. These phases included in the Ti-based alloy systems are defined as α, β, near-α, near-β, etc. Therefore, titanium alloys have been able to achieve excellent properties as a result of their chemical compositions [6]. There are three types of base titanium alloys mainly identified as α, β, and α + β alloys according to their phase stability.

Despite this, research has shown that there is a correlation between the overall properties of the alloy and the level of impurities it contains. Impurities adversely affect the plasticity of the alloy. There is a plastic deformation associated with titanium alloys when hydrogen, carbon, oxygen, and nitrogen combine with them [7]. As there are so many possible compositions and it is not possible to screen them in a practical manner using a random combination of these elements, there seems to be a compelling need for new approaches that will enable us to make efficient choices of compositions for the manufacturing of titanium alloys, and other advanced alloys of high performance. The interaction between the elemental composition of titanium alloys, their manufacturing techniques of them, and their microstructure of the alloys must be taken into account when determining the properties of titanium alloys and when they are being designed to get the best results. It is therefore imperative that one utilizes the correlation between the microstructure, properties, and performance of titanium alloys in order to gain a more comprehensive understanding of them. As a result of this correlation, it is expected that it will provide an opportunity to develop novel designs as a result of this correlation [8].

It has been demonstrated in this study that it will be feasible to use a new approach that will give correlations between the evolution of microstructure and properties of alloy systems and the interdiffusion properties of their compositional elements to arrive at a new approach to the problem. Evidence exists that indicates that titanium alloys obtain their strength as a result of the strengthening of the solid solution and grain refinement.

2. High-throughput materials characterization techniques

2.1 Mapping spatial data using statistical techniques

In order to characterize an alloy, the intrinsic heterogeneity of the material is utilized as a basis for a statistical spatial mapping technique that allows high throughput. There are tens of thousands of material microarrays that are being used to obtain different compositions, structures, and properties of a material through the cross-scale characterization of the material. It is necessary to formulate a statistical spatial-mapping model between the two sets of parameters based on the original material.

With the aid of high-throughput computational studies, it is possible to create databases by screening lattice units, which determine the properties of screened material with the help of high-throughput computations, and then creating a database of materials once the materials have been screened, and after the materials have been screened, the database can be created. There are numerous types of materials-design optimization strategies that have been developed to guide new materials discovery, process optimization, and material modification, as shown in Figure 1.

Figure 1.

It is a statistical spatial mapping technique based on the heterogeneity of the materials to generate the maps [9].

A Ti-alloy can have slightly different compositions, structures, as well as properties at different points of its structure, and the arrangement of these small differences determines the overall quality of the alloy as a whole, which is determined by the combination of these small differences. A wide range of rapid characterization techniques can be used to gather data from the macroscopic to the microscopic scale for the purpose of high-throughput statistical spatial mapping on a micron level. In order to meet practical sample sizes, fast and reasonable turnaround times are required. This is in order for composition, structure, and property datasets to be gathered at each of the locations. A database can be constructed based on the data entered into it, which contains spatial mapping lattices, based on precisely placed positional coordinates and references to point-to-point correspondence, in order to construct a spatial mapping map. The spatial mapping datasets are selected from a database within the required target intervals based on requirements for material research and development.

In order to determine a suitable design that is more likely to meet the targeted requirements based on a statistical analysis of the data, a statistical analysis can be performed in order to determine the appropriate design. A number of studies have demonstrated that optimizing process parameters allows the assembly of these genetic units at the mesoscale to be verified, and quantitative correlations have been established between the micro-, meso-, and macroscales, as well as between practical samples and across the spectrum composition, structure, and properties. Recent years have seen the use of high-throughput statistical spatial mapping techniques to characterize a variety of material systems, including a wide variety of titanium alloys [10].

An alloy is a material that is heterogeneous, multielementary, and complex in structure. As a result, the structural composition, properties, and structure of an alloy may differ slightly at different points within it, and it is the amalgamation of these variances that determines the global functioning of the alloy. As a building block, a unit cell arrangement is used to provide insight into the properties of a material. The arrangement of unit cells is therefore critical for understanding the material and determining its properties at nanoscale. It is therefore possible to establish a correlation between the microscales, mesoscales, macroscales, and across-scale spans, as well as the compositional information, the structural information, and the properties of those spans, so as to enable the creation of novel materials and the amendment of current materials efficiently and economically. Rapid measurement of compositional information, structural information, and properties related to application at multi-locations are performed in order to obtain practical sample sizes based on the available data. With the use of accurate positional coordinates, as well as point-to-point communication, a database is created to represent spatial mappings. Spatial mapping datasets are selected based on the target intervals as part of the design requirement of developing new materials. Based on a variety of factors, a variety of statistical analyses may be used to determine which design is best suited to meet the targeted requirements. Several criteria can be used to determine which design is best suited to meet the intended requirements, such as metrics and models that can be used to determine the frequency of occurrence within the range of parameters, the correlation ratio between parameters, and the statistical elimination of outliers. Many researchers used the process and used advanced microscopy and spectroscopy for data acquisition and statistical distribution analysis [11, 12].

2.2 Diffusion-multiple approach

In order to produce sizeable, multicomponents compositional deviations in alloy system samples through thermal interdiffusion, Zhao developed the diffusion multiple technologies, based on diffusion couples that generate compositional variations in bulk samples through diffusion [13]. Various experimental and analytical tools can be used to analyze diffusion multiples to extract the dependence of structure and properties on components. The application of the novel procedure significantly enhanced both the competence of elemental compositional design as well as the screening of appropriate heat treatment practices in comparison with the traditional methods that use a single alloy model to analyze the advancement rules of properties and microstructural information.

The infusion of a variety of alloying elements into titanium alloys can be investigated to determine how they affect their structure and properties by using combination of different diffusional multiple elements. It is, therefore, important and necessary to use diffusion multiple methods to study titanium alloys in order to achieve the best results [14].

It is possible to investigate kinetics, phase diagrams, and compositional-structural-properties relationships of alloy systems by using the diffusional multiple approaches, which uses the formation of compositional gradients and phase developed by long-term annealing of the alloy [15]. In order to determine diffusion coefficients and phase diagrams, conventional diffusion pairs and diffusion triples have been used for more than three decades. It has previously been demonstrated that it is possible to determine a number of composition-structure–property relationships by performing localized microscale property measurements on single-phase compositions [16].

Many systems have demonstrated the ability of the diffusion-multiple approach to be used as a tool for determining very complex phase diagrams, and this has been demonstrated for many different systems. In order to compare the phase diagrams of simple and very complex ternary systems, diffusion multiples have been used in place of equilibrated alloys to determine the phase diagrams [17]. In light of the results of this experiment, it can be concluded that the phase diagrams which have been determined from diffusion multiples are of very high accuracy [18].

As a result, a diffusion multiple analysis is a method that can be used to analyze diffusion data and to predict the microstructure and properties of alloy compositions using diffusion data for a variety of alloy compositions using the diffusion data as an input.

2.3 Computational thermodynamics using CALPHAD

CALPHAD is a computational thermodynamics program that can be used to compute and develop phase diagrams. It is commonly used for designing and developing new alloy systems [19]. In order to achieve the desired properties and consequently potential applications, it is important to examine the phase structure and phase equilibrium of the alloy systems. In the strategy of novel and advanced alloy systems, one of the advantages of using CALPHAD over entropy alone is its ability to analyze phase formation using free energy rather than entropy alone. This allows CALPHAD to better understand the function of system enthalpy, as well as the function of the entropy in the design of the alloy system [20].

A phase diagram provides detailed information on microstructural phase information as a function of its compositional information, its temperature information, and its information related to pressure. As such, it serves as a guide when designing and developing new Ti alloys. CALPHAD has been proven to be an effective tool for estimating the phases present in titanium alloy systems based on extensive research using titanium alloys. In spite of this, this method still did not yield enough screened titanium alloys which were able to produce the phases required in the temperature range of interest [21].

As a result of this, the thermodynamic databases in CALPHAD continue to grow as more and more experiments are conducted. Therefore, the accuracy of CALPHAD is also expected to increase as the number of experimental data continues to increase. It is expected that high-throughput CALPHAD simulations will be able to provide more accurate and reliable results for creating and optimizing titanium alloy compositions based on desired alloy properties because of more reliable and accurate simulation studies.

2.4 Machine learning and statistical methodology approach

This approach applies machine learning to a variety of different approaches, such as deep learning. This is a subset of machine learning, which is a subset of what we refer to as artificial intelligence. This is often referred to as artificial intelligence. This refers to a computer system’s capability of learning from the inputs it receives. This allows it to improve itself in order to find out how to do things better in the future. In the field of alloy design and development, artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible computational solution to overcome the challenges of alloy design and development, as well as control the costs and speed of processes in alloy design and development through artificial intelligence [14].

Recent years have seen a rise in interest in alloy development research centered on this approach. With the use of deep learning and machine learning algorithms, it will be feasible to rapidly transform large quantities of experimental data into usable feature information. With the aid of learning algorithms, it is possible to develop computer models that quickly generate judgment results based on input data. In addition, it is possible to addition, it is conceivable to extract information from alloy systems based on the prior knowledge of the system controllers.

Using high-throughput experiments and algorithms based on machine learning, Zhu et al. [14] have developed a titanium alloy using high-throughput experimental techniques. In the field of titanium-based alloy systems, the artificial neural network techniques, as traditional machine learning methods, have been successfully used for a variety of functions, such as predicting properties such as flow stresses, evolutions of microstructures, mechanical properties and parameters that affect during the processing of titanium alloys [22, 23, 24].

It has been discovered that when one technology is combined with machine learning, the screening of alloys becomes more efficient. A machine learning algorithm can be used to envisage the microstructure of an alloy and the anticipated results can be equated with those of the experimental results. Zhu et al. [14] reported the findings of a applied diffusion multiple in combination with machine learning algorithms to formulate a new Ti-based alloy system (Ti-3Al-2Nb-1.2 V-1Zr-1Sn-4Cr-4Mo). After the solution was heat-treated at 750°C for 6 hours and the material was aged at 550°C for 6 hours. In the study, researchers reported that better strength and plasticity could be obtained. The evidence suggests that the globular primary α phases elongated during deformation, while the secondary acicular α phases resist dislocation sliding, therefore, providing both a high degree of plasticity and strength for the alloys, that are subjected to the deformation [14].

3. Effect of alloying elements on properties

Various alloying elements are present in titanium alloys. The role of these alloying elements is to strengthen them either in low-temperature phase or in high-temperature phase, depending on the alloying elements. The alloying elements, in varying proportions, stabilize the close-packed hexagonal alpha (a) phase at low temperatures and the body-centered cubic beta (b) phase at high temperatures. It is their contents that determine the morphology and distribution of these phases. It is known that the alpha phase is a solid solution-strengthened phase that is stabilized by aluminum. This increases tensile and creeps strength. Tin is used in conjunction with aluminum to provide strength without embrittlement. Up to 5% of zirconium increases strength at low to intermediate temperatures. As the oxygen content in the titanium alloy increases, the ductility, toughness, and high-temperature strength of the titanium alloy decreases [25].

The high-temperature beta phase is stabilized by molybdenum, which increases the short-term strength at high temperatures. As well as being a beta-phase stabilizer, Niobium is also added to improve the stability of the surface at high temperatures. At all temperatures, silicon increases the strength as well as the creep resistance of titanium alloys. The other trace elements, such as chromium, cobalt, and nickel, are not beneficial for creep, and their contents are restricted to less than 0.01 percent [26].

Several studies have been conducted on the biocompatibility of titanium alloys for applications such as biomedical implants containing molybdenum, tantalum, and niobium, and on these bases, the developed alloys are Ti-Mo-Zr-Ta, Ti-12Mo-5Ta, and Ti-Nb-Zr-Mo [27, 28].

3.1 Case study on Ti-based alloy

It is important to realize that Ti-based alloy systems possess high-temperature mechanical properties, making them a very important group of structural materials that can be used in a wide range of strategic applications. These two-phase alloys are used for advanced engineering purposes, incorporating third alloying elements to enhance the ductility and strength, and maintain the properties at elevated temperatures. This is a two-phase lamellar structure consisting of alternate layers of tetragonal (L10) and hexagonal (D019) phases that consist of titanium and alloying elements in alternate layers. It is important to note that the optimum volume fraction for lamellar structure leads to an exceptional level of ductility that is virtually nonexistent in pure alloys. As a result of the process of plastic working, as well as the heat treatment, the microstructure of these alloys can be significantly altered in order to achieve a finely tuned mechanical property as well as fatigue behavior depending on the application [29]. There are a variety of mechanical properties depending on the morphology and the distribution of phases. As a result, the mechanical properties of titanium alloys with two phases are strongly influenced by the morphology of each phase. Many factors can affect the strength of an alloy with a lamellar microstructure; however, the thickness and diameter of the lamellae have the greatest impact [30]. In order to improve the mechanical properties of different alloys, the volume fractions, distribution, and morphology of the different phases play a critical role. Ti, Al, Cr, and Nb make up the elemental composition of the Ti-base alloy with nominal chemical compositions of Ti-40Al-2Cr-2Nb-0.4Y-0.2Zr, which has been used in this particular case. After one-hour heat treatment at 1350°C, the samples are furnace cooled to room temperature.

The engineering stress and strain and true stress–strain diagrams are shown in Figure 2. In fact, the true stress and strain values are very high because a smaller cross-sectional area is being used, whose section decreases continuously during elongation. True stress values indicate that, unlike engineering stress–strain values, material becomes stronger as strain is increased, compared to engineering stress–strain values. An alloy’s mechanical properties can be greatly affected by the size of the colonies of crystallographically oriented lamellae within the alloy since it is a measure of the effective length of the slip that affects the alloy’s mechanical properties. In spite of this, the transition to ‘basket weave’ microstructures will mean it will be even more challenging to determine the size of colonies as they emerge. Therefore, in order to illustrate the effect of microstructure refinement on mechanical properties, the thickness of lamellae was also taken into account as a quantitative parameter to illustrate the effect [31].

Figure 2.

A comparison of (a) engineering stress-strain curve and (b) true stress-strain curve of as-forged and solution heat-treated Ti-based alloys.

Strain hardening is the property of materials that exhibits this property. As part of the forming process, stain hardening (work hardening) plays an important role. Observing the plot, it was evident that stress rises without showing a drop in yield, as indicated in the figure. It can therefore be concluded from the shape of the true stress–strain curve that a material is prone to fracture before it is prone to yield, based on the shape of the curve.

The optical micrographs of as-forged and solution heat-treated Ti-alloy are shown in Figure 3. The lamellae structure of the annealed sample at 1350°C can be seen as having a random orientation due to the annealing process. This lamellae structure consists of alternate layers of the alloys γ- and α-phases. The solution heat-treated Ti-alloys has shown better property than forged alloys. This is due to the fact that the load-transferring capacity of lamellae is greater than that of duplex grains and near grains. A colony size of 80–100 μm was found to be the maximum size of the lamellae in the colony.

Figure 3.

Optical micrographs of (a) as-forged, and (b) solution-treated Ti-base alloy.

It can be seen in Figure 4 that Ti-alloy has an even microstructure in an as-forged condition, which consists of equiaxed grains of γ and α phases and alternate plates of α and γ phases. Depending on the sample’s history, the morphology of the grains differs from one sample to another. In the present case, the dislocations are thermal in origin. Several second-phase particles larger than 500 nm are usually found on the grain/interphase boundaries. It is worth noting that there is a wide variation in the grain size in this multiphase microstructure. A few of the grains have a size of less than a micrometer, and there are a few others that are larger.

Figure 4.

Transmission electron microscope (TEM) micrographs of as-forged Ti-alloy in bright field mode showing equiaxed grains of γ and α phases and alternate plates of α and γ phases. The morphology of the phases depends on their history, or at least on the stage of their origin in the evolutionary process, which determines their morphology.

Figure 5 shows a dark field TEM micrograph of an alpha grain that is disordered. While the formation of α₂ from α is taking place, there are a number of finely ordered domains that are being formed, which are more apparent at the outset.

Figure 5.

A dark field TEM micrograph showing the transformation of α → α₂ in disordered α grains showing the transformation in a blown out image. There are a number of the finely ordered domain during the formation of one α to another α₂, which are more evident at first.

The deformation mechanism is also identified in the Ti-alloy. There is a high density of dislocations in the γ-phase, while there are very few dislocations in the α₂ phase. There is a great deal of difficulty in deforming the ordered alpha by dislocation slip. It is caused by the slitting of the dislocations, which causes them to become super-dislocations. Super dislocations, as it is well known, require a greater amount of energy in order to move forward.

There is a deformation of the ordered α₂ phase by twinning. The disordered α phases are a high-temperature phase, which when cooled to room temperature decomposes to the ordered α₂ + γ at room temperature. A crucial aspect that we have collected in our research is the deformation characteristics of the constituent phases, which has provided us with invaluable information. A comparison should be made between the mechanical properties and microstructures of the selected alloy with a few other alloys that have been identified, and similar tests should be performed on those alloys that have been identified. By performing this comparative study, it will be easier to identify which sample is the best of those that have been tested. In addition, these alloys will also be able to provide an idea as to how to further improve the alloy design in Ti-base alloy systems by adding alloying elements or choosing the process of heat treatment, etc. which will result in better alloys.

4. Conclusions

It is evident from this article that the screening of alloy properties as well as microstructural stability is a substantial undertaking that requires a considerable amount of time and effort. In order to simplify the selection of titanium alloy compositions, a high-throughput-based alloy design approach is used. Different high-throughput methods have been used to calculate diffusion coefficients for a number of different elements, using a database of atomic mobility as a basis for calculating diffusion coefficients.

As a result of applying a rigorous screening criterion and evaluating it in an experimental setting in order to come up with the optimal composition, an optimal composition is selected that has comprehensively optimal properties. As compared to the data-driven materials design methods that have been used in recent years, few methods are more accurate and efficient, mainly because diffusion pairs, atomic mobility databases, and refined physical models work together so as to make this strategy the most accurate and efficient.

This approach is believed to be able to enable the development of high-performance titanium alloys regardless of the composition of the alloy, which is believed to be beneficial in overcoming the challenges that are associated with the development of novel titanium alloys for applications in high-temperature structural applications.