Microstructure Of Titanium Alloy Research Articles

Jet Electrochemical Micromilling of Ti-6Al-4V Using NaCl-Ethylene Glycol Electrolyte.

Titanium alloys are widely used in aerospace and biomedicine because of their excellent mechanical characteristics, but these properties also make such alloys difficult to cut. Jet electrochemical micromilling (JEMM) is based on the principle of electrochemical anodic dissolution; it has some inherent advantages for the machining of titanium alloy microstructures. However, titanium oxidizes readily, forming an oxide film that impedes a uniform dissolution during electrochemical machining. Therefore, a high voltage and an aqueous NaCl electrolyte are usually used to break the oxide film, which can lead to severe stray corrosion. To overcome this problem, the present study investigated the JEMM of Ti-6Al-4V using a NaCl-ethylene glycol (NaCl-EG) electrolyte. Electrochemical testing showed that Ti-6Al-4V exhibits a better corrosion resistance in the NaCl-EG electrolyte compared to the aqueous NaCl electrolyte, thereby reducing stray corrosion. The localization and surface quality of the grooves were enhanced significantly when using JEMM with a NaCl-EG electrolyte. A multiple-pass strategy was adopted during JEMM to improve the aspect ratio, and the effects of the feed depth and number of passes on the multiple-pass machining performance were investigated. Ultimately, a square annular microstructure with a high geometric dimensional consistency and a smooth surface was obtained via JEMM with multiple passes using the optimal parameters.

Efficient and accurate segmentation of adhesive lamellae in tri-modal microstructure of titanium alloy based on OpenCV

The adhesive characteristics of the tri-modal microstructure in titanium alloys, particularly the adhesion of the lamellar phase that constitutes over 50% of the microstructure, significantly hinder its quantitative characterization, further impeding the establishment of the quantitative relationships between the microstructure and mechanical properties. Addressing this challenge, this study utilized the OpenCV computer vision library to accomplish efficient and precise automated segmentation of the adhesive lamellae in tri-modal microstructure by enhancing the existing segmentation algorithm based on concave point matching. Specifically, by taking the number of the adhesive target after segmentation and Harris corner detection threshold as the optimization goal and variable respectively, and employing the genetic algorithm, the threshold, accounting for the segmentation effect, was adaptively determined to accurately identify the corner points of the adhesive lamellae. And then quick determination of the concave points within these corner points was achieved by substituting conventional pixel circle with equidistant pixels distributed along the circumference. Upon identifying the concave points, classification of the points was performed based on their spacing and location, and corresponding matching criteria were established and applied. Eventually, by introducing segmentation lines between the matched points, adhesive lamellae were segmented with an accuracy exceeding 85%, which will lay a robust foundation for the rapid and automated quantitative characterization of the tri-modal microstructure in titanium alloys.

Dependence of the Formation of the Microstructure in Titanium Alloys on Electroplastic Rolling Modes

Dependence of the Formation of the Microstructure in Titanium Alloys on Electroplastic Rolling Modes

A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Microstructures play a central role in determining the mechanical and functional properties of a material. An important aspect in computational materials science is to reliably predict the key microstructural features, and to utilise them in bridging the processing and properties of new materials. The existing microstructure characterization and reconstruction (MCR) techniques have inherent limitations in terms of the lack of design variables and information loss due to various assumptions. In previous studies, Generative Adversarial Network (GAN) models were trained using more than 10,000 image datasets, however without performance analysis using quantitative morphometric and statistical measures. In this perspective, the present work demonstrates the capability of GAN architectures to learn the mapping between random latent vectors and synthetic microstructural images, even in a limited data regime (1225 images). Three different architectures Deep Convolutional GAN (DCGAN) ,Wasserstein GAN- Gradient Penalty (WGAN-GP), StyleGAN2- Adaptive Discriminator Augmentation (ADA) were explored, together with comprehensive statistical and morphological analysis, while training on a publicly accessible Ti-6Al-4V (Ti64) alloy microstructure dataset. The StyleGAN2-ADA outperforms the other two GAN models to generate realistic synthetic microstructures of higher resolution, with good qualitative similarity to original images. The analysis of performance metrics, like Frechet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS) scores, reveal that the generated image distribution is statistically close to the original distribution of microstructural features. The morphometric parameters, including α/β phase fractions, local α/β boundary thickness, and orientation of lamellar morphology, were also used to compare original and synthetic images quantitatively. More importantly, two-point correlation and t-stochastic neighbour embedding (t-SNE) illustrate the statistical similarity between the original and synthetic microstructures. Taken together, the present work establishes the capability of generative models like GAN in generating representative microstructures of Titanium alloy in a statistically reliable manner. Such an approach, when adopted, will accelerate the field of microstructure fingerprinting.

Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentation

The mechanical properties of the materials are determined by the size and morphology of fine microscopic features. Quantitative microstructural analysis is a key factor to establish the correlation between the mechanical properties and the thermomechanical treatment under which material condition has been achieved. As such, microstructural analysis is a very important and complex task within the manufacturing sector. Published standards are used for metallographic analysis but typically involve extensive manual interpretation of grain boundaries, resulting in measurements that are slow to produce, difficult to repeat and highly subjective. Computer vision and the evolution of artificial intelligence in the past decade can offer solutions to such problems. Deep learning and digital image processing techniques allow digital microstructural analysis to be automated using a fast and repeatable method. This paper proposes a novel boundary class semantic segmentation approach (BCSS) to identify each phase of the microstructure and additionally estimate the location of the grain boundaries. The BCSS is then combined with more traditional segmentation techniques based on the Watershed Transform to improve the identification and measurement of each feature within the microstructure using a new, hybrid automated digital microstructure analysis approach. The new method is validated on a published dataset of two-phase titanium alloy microstructure pictures captured using a scanning electron microscope. Measurements match the level of accuracy of accepted manual standards, and the method is demonstrated to be more reliable than other automated approaches. The influence of the subjective nature of manual labelling, required to train the proposed network, is also evaluated.

Design of bimorphic-bimodal microstructure in titanium alloys by semi-solid sintering

We have innovatively achieved bimorphic-bimodal microstructures (BBMs) by semi-solid sintering of TiNbNiCu(Al/Sn) amorphous alloy powders. The BBM contains equiaxed ultrafine-grain matrix, which inherits crystallized equiaxed microstructure after solid sintering, of fcc MTi2 (M = Cu, Ni) embeded into bcc β-Ti, accompanied by isolated regions with coarse β-Ti surrounded by ultrafine-grain fcc MTi2 shell, which originate from fast growth of crystallized β-Ti formed during solid sintering and rapid cooling of fcc MTi2 melt during semi-solid sintering. Resultantly, the BBMs exhibit high an ultimate compressive strength of 2567 MPa and a large plastic strain of 38.1%, superior to those of similar titanium alloys fabricated by melt solidification. The excellent mechanical properties are attributed to the multiplication of slip bands, which are formed in bcc β-Ti phase regions and intercepted by fcc MTi2 phases in the BBM titanium alloys. We anticipate that our work could provide a new strategy for designing lightweight and high-performance materials.

Neural network modeling of titanium alloy composition-microstructure-property relationships based on multimodal data

The composition and microstructure of metallic materials determine their mechanical properties. Establishing quantitative “composition-microstructure-property” relationships is essential for material design and optimization. However, materials data are complex and heterogeneous, often containing multi-modal information such as “point-image-curve”. Traditional mathematical models and simulations can no longer meet the needs of advanced applications. Big data technology provides a new approach to materials research, and machine learning tools can offer high-dimensional information in a low-order form. In this study, a neural network-based titanium alloy microstructure identification model and a performance prediction model have been designed. A model for heterogeneous data of titanium alloy composition (data), microstructure (image), and performance (curve) has been established through the fusion of the two models. The cross-dimensional prediction of the material stress-strain curve using composition and microstructure image data has been successfully achieved. A four-step workflow of dataset preparation, pre-processing, model building, and fusion is determined. The task of identifying 1215 images of 14 titanium alloys and predicting 604 performance curves has been completed. This work demonstrates the process of establishing the “composition-microstructure-property” relationship of materials by big data technology.

Constitutive Behavior of Titanium Alloy With Dual-Phase Microstructures: Experiments and Modeling

AbstractThe complex composition, size, and distribution of microstructures of titanium (Ti) alloy affect the mechanical properties of titanium alloy and its application in aerospace, ocean technology, and bioengineering. In this paper, the microstructural components and mechanical behavior of Ti80 are first investigated experimentally. According to the experimental observations of the dual-phased microstructures, a mechanism and microstructure-based constitutive model of Ti80 is established to study the quantitative relationship between mechanical behavior and equiaxed αp + lamellar αs + β microstructures of titanium alloys. And the influence of dislocation evolution and accumulation on the strengthening and work-hardening of materials is also explored in detail, especially the contribution of dislocation pile-up zone at the phase boundary between α phase and β phase on the strengthening of materials. Numerical results show that the proposed model can describe the constitutive behavior of Ti80 very well, including yield stress and strain hardening. And various strengthening mechanisms originated from the grain boundaries, phase boundaries of β transformation structure and β precipitation are analyzed. The proposed model is further applied to predict the constitutive behaviors of the titanium alloy with different sizes and various volume fractions of microstructure.

Dislocation Slip and Crack Nucleation Mechanism in Dual-Phase Microstructure of Titanium Alloys: A Review

Dislocation Slip and Crack Nucleation Mechanism in Dual-Phase Microstructure of Titanium Alloys: A Review

Wear Behavior of Ti6Al4V Based Hybrid Composites Reinforced with TiB2(7.5%) + TiC (7.5%)

Titanium matrix composites (TMCs) have a greater capacity to realize needs and wishes in aviation, modern firearm systems, and the locomotive sector due to the outstanding specific strength, higher temperature stability, particular stiffness, and resistance to corrosion. However, due to weak tribological characteristics and low hardness, titanium and its alloys' applicability in harsh friction and wear environments are severely constrained. The goal of this research is to improve the wear properties of Ti6Al4V-based hybrid composites reinforced with TiB2(7.5%) + TiC (7.5%). The dry sliding characteristic of powder metallurgy Ti6Al4V alloy and Ti6Al4V/ TiB2(7.5%) + TiC (7.5%) powder metallurgy composites was examined. The wear and friction behaviour of TMCs are explored utilising a pin-on-disk wear tester in a variety of circumstances. The results show that when the sliding speed increases, the particular wear rate increases as well. With the assistance of a Field Emission-Scanning Electron Microscope (FE-SEM) and Energy Dispersive Spectroscopy(EDS), the impact of TiB2 and TiC enhancement on titanium alloy microstructure as well as wear parameters were investigated. The wear resistance of the Ti6Al4V matrix was found to be increased by adding TiB2 and TiC to the sterilised Ti6Al4V matrix.

Tailoring multi-scale hierarchical microstructure parameters to achieve super-high strength and considerable plasticity in a dual-phase Ti-5.2Mo-4.8Al-2.5Zr-1.7Cr alloy

In the present study, traditional treatment was designed specially and carried out on a dual-phase Ti-5.2Mo-4.8Al-2.5Zr-1.7Cr alloy, and a multi-scale hierarchical microstructure differed from the ordinary microstructure of titanium alloys was obtained. During the solution-treatment at 900 ℃ for 1 h followed by furnace cooling (FC), air cooling (AC), water cooling (WC) and at 920℃for 1 h followed by WC, three equiaxed microstructures composed of primary equiaxed α phase (αp) and β phase with different fractions, and a typical bimodal microstructure containing primary αp and acicular secondary α phase (αs) were formed. During the aging-treatment at 550 ℃ for 6 h followed by AC after solution-treatment at 900 ℃ for 1 h followed by WC, a multi-scale hierarchical microstructure composed of equiaxed αp, acicular αs with a thickness of dozens of nanometers, smaller acicular α phase (αs-s) with a thickness of ten nanometers, distributed in the space of two αs, and whisker β phase (βw) in the transformed β (βt) regions, was obtained. Moreover, with the solution temperature increased to 920 ℃, it was deliberately designed that equiaxed αp was more refined, and finer and denser acicular αs-s precipitated in the alloy. Contributing to the equiaxed αp refinement and nano-scale acicular α phases, the hierarchical microstructure exhibited super-high yield strength of 1228 MPa and ultimate tensile strength of 1389 MPa. Meanwhile, the refined equiaxed αp could also offset the negative effect of acicular α phases on plasticity, and the plasticity was maintain at an considerable level of 8%.

Interrelationship of Fracture Mechanism and Microstructure of TC18 Titanium Alloy

The scanning electron microscope (SEM) and optical microscope (OM) were used to study the deformation of TC18 titanium alloy microstructure at 881°C and 896°C. And the basket weave structure fracture mechanism was discussed. The results have been shown: during deformation at 881°C, the TC18 titanium alloy β grain size of about 305μm and the discontinuous grain boundary α phase along the β grain boundary were obtained. With the deformation temperature rising to 896°C, the β grain growth of 510μm and the continuous grain boundary α phase along the straight β grain boundary were obtained. The TC18 titanium alloy fracture toughness decreased from 77.8 MPa·m1/2 to 65.4 MPa·m1/2 as the rising of deformation temperature from 881°C to 896°C. The average β grain size is about 305μm and the discontinuous grain boundary α phase leads to the higher fracture toughness of TC18 titanium alloy forging. The fracture mode of fine β grain and discontinuous grain boundary α phase is the transgranular fracture, while the coarse β grain and continuous grain boundary α phase is the intergranular fracture.

A comparative study on fatigue indicator parameters for near‐α titanium alloys

AbstractNucleation of in‐service cracks leads to detrimental consequences for structural components of near‐α titanium alloys subjected to fatigue loads. Experimental observations show that the fatigue initiation facets usually form in certain crystallographic orientation ranges of “hard” primary α grains which differ between pure and dwell fatigue loads. In this manuscript, a comparative study has been performed using several fatigue indicator parameters (FIPs) to assess their ability to predict the location of fatigue crack nucleation in near‐α titanium alloy microstructures. All selected FIPs are implemented within the same polycrystalline plasticity finite element modeling framework to facilitate one‐to‐one comparisons. Comparison on predictability of critical initiation locations and their crystallographic orientations is studied for incorporated FIPs under pure and dwell fatigue. The critical locations predicted by some FIPs were found to be close to each other, and consistent with the crystallographic orientation ranges from fractography measurements, in addition to the range transition from pure to dwell fatigue loads. Critical locations from slip driven FIPs are obtained to be several grains away from that of the former ones and are inclined to capture orientations of slip traces from experiments.

Heterogeneous precipitate microstructure in titanium alloys for simultaneous improvement of strength and ductility

• We propose a step-aging method to improve the comprehensive mechanical properties of titanium alloys by designing heterogeneous microstructures through simulations and experiments. • The formation of heterogeneous microstructure can be attributed to the integration of conventional nucleation and growth, and non-conventional pseudospinodal decomposition mechanisms. • Ti-1023 (Ti-10V-2Fe-3Al) alloy with heterogeneous microstructure has shown enhanced ductility by ∼50% and strength by ∼10% comparing with that of homogeneous microstructure. • Both deformation twin and dislocation slip for heterogeneous microstructure have been activated and contributed to the enhanced mechanical properties. The design of alloys with simultaneous high strength and high ductility is still a difficult challenge. Here, we propose a new approach to designing multi-phase alloys with a synergistic combination of strength and ductility by engineering heterogeneous precipitate microstructures through the activation of different transformation mechanisms. Using a two-phase titanium alloy as an example, phase field simulations are carried out firstly to design heat treatment schedules that involve both conventional nucleation and growth and non-conventional pseudospinodal decomposition mechanisms, and the calculated microstructures have been evaluated by crystal plasticity finite element modeling. According to simulations, we then set a two-step heat treatment to produce bimodal α + β microstructure in Ti-10V-2Fe-3Al. Further mechanical testing shows that the ductility of the alloy is increased by ∼50% and the strength is increased by ∼10% as compared to its unimodal counterpart. Our work may provide a general way to improve the mechanical properties of alloys through multiscale microstructure design.

Phase-field study on the formation of α variant clusters induced by elastoplastic stress field around the void in titanium alloys

The formation of α variant clusters during β to α transformation under the elastoplastic stress field around a void was investigated using J2 plasticity theory and phase field simulation. Compared with elastic materials, taking an elastic–plastic phase field model in the present work, the stress fields around the void can be partially relaxed by plastic deformation, and the maximum amplitude of the stress reduces. With the increase of the void size, the selection effect on α variant clusters in titanium alloys is gradually enhanced. The variant selection is asymmetric under the conditions of applied tensile and compressive stresses, and the influence of the stress fields around the void under external shear stress is stronger. These could be predicted and confirmed by the interaction energy calculations between the stress fields around the void and α variants. Some interesting variant clusters appear, such as “windmill”, “butterfly”, and “wing” types under external tensile, compressive and shear stresses respectively. The “equilateral triangle”, “nearly parallel” and “quasi equilateral triangle” type variant clusters can form through sympathetic nucleation, given the particular α/α orientation relationship corresponding to lower energy. The ultimate microstructures of α variant clusters around a void are determined by both inter-variant and variant-external stress field interactions. The high-throughput calculations under different applied stress states would provide support for the generation and optimization of the microstructures of titanium alloys in follow-up work.

Corrosion behavior of near‐alpha titanium alloy fabricated by additive manufacturing

AbstractThe purpose of this study was to investigate the corrosion behavior of Ti–6Al–Mo–1.5V–2Zr near‐alpha titanium alloy fabricated by additive manufacturing (AM). Titanium alloy specimens were fabricated by electron beam melting (EBM) and laser metal deposition (LMD). The same titanium alloy manufactured by traditional technology (TT) was used as a control. The correlations between corrosion resistance, microstructure and phase composition of titanium alloys fabricated by different technologies were investigated, through the use of electrochemical corrosion testing, scanning electron microscopy, X‐ray diffraction, and hardness testing. In this study, it was shown that the corrosion resistance of AM samples is lower than TT samples. The corrosion resistance of AM samples was attributed to the presence of more α′ martensite and less β‐Ti phases in the microstructure of titanium alloy than for TT samples. The electrochemical results suggest that titanium alloy fabricated by EBM has better corrosion resistance in 20% HCl solution at room temperature compared to titanium alloy fabricated by LMD.

Study of simulation parameters of refining solidification microstructure on Ti-55531 via a suction casting

Ti-55531 is difficult to achieve samples by a vacuum suction casting. Therefore, the experimental parameters of the suction casting are simulated, and the properties of Ti-55531 alloy with the microscale refining solidification microstructure are studied. The results show that the preparation parameters simulated through the ProCAST on Ti-55531 are obtained. Pouring temperature of 1662 °C is selected as the experimental temperature. Suction casting speed of 2 m/s is selected owing to it with a greater influence on the filling rate. Model temperature of 20 °C is selected with the low temperature. The copper casting mold is selected as the heat transfer coefficient of mold has very small effect on filling rate. The solidification time is not significantly affected by the pouring temperature and the mold temperature. The properties of Ti-55531-0.1 % La are further improved, due to the solidification structure with the microscale refining solidification microstructure for an average spacing of 3.6 μm in the edge. The hardness and strength in the edge both are 11.1 % higher than that in the centre due to refined structure. This study provides a new idea for the formation of a fine microstructure of titanium alloy.

Achieving an ideal combination of strength and plasticity in additive manufactured Ti–6.5Al–2Zr–1Mo–1V alloy through the development of tri-modal microstructure

A better combination of strength and plasticity may be imparted by the tri-modal microstructure in titanium alloys than the as-built acicular martensite. It is generally conceived that hot working is required for the globularization of the α phase in traditional manufacturing. In this article, we report an innovative strategy to obtain a tri-modal microstructure consisting of globularized α in laser powder bed fusion (L-PBF) Ti–6.5Al–2Zr–1Mo–1V (TA15) alloy, which only consists of post-process heat treatment. A tri-modal microstructure formed by the equiaxed α, lamellar α, and transformed β matrix may be effectively introduced by the multiple annealing heat treatment. A significant improvement in ductility by 72.32% was obtained with final microstructure leads, maintaining a desired high strength and providing tensile properties comparable to those of its forging counterparts.

Quantitative analysis of globularization and modeling of TC17 alloy with basketweave microstructure

The globularization behavior and mechanism of TC17 alloy with basketweave microstructure were investigated, and the models of dynamic and static globularization kinetics were established. The quantitative and metallographic results show that the globularization of α phase is sensitive to the parameters of deformation and heat treatment. By EBSD analysis, the formation and evolution mechanisms of intra-α boundaries are related to discontinuous dynamic recrystallization and continuous dynamic recrystallization, which can form α grains with high and low misorientations between neighbour grains after the heat treatment, respectively. Based on the globularization behavior and mechanism, two modified JMAK models are developed to predict the dynamic and static globularization kinetics, and the mean absolute relative errors (MARE) of 10.67% and 13.80% indicate the accuracy of the dynamic and static globularization kinetics models. The results of this work can provide guidance for controlling microstructure of titanium alloy.

Effect of additive manufacturing process parameters on the titanium alloy microstructure, properties and surface integrity

Damage initiation and propagation are widely impacted by pores distribution induced during additive manufacturing (AM) processes. More specifically, the porosity can strongly affect the thermomechanical behavior of titanium alloys in the case of Selective Laser Melting (SLM). The present work examined the impact of SLM process parameters on microstructure characteristics, mechanical properties, and surface integrity of the Ti6Al4V titanium alloy. Different metallographic analyses (i.e., Scanning Electron Microscopy (SEM), and X-Ray Diffraction (XRD)), density measurement and surface characterizing were carried out to investigate the effect of various process parameters (laser power and scanning speed) on the damage behaviors in terms of fracture surface and cracks initiation sites of the alloy.