Microstructure Of Titanium Alloy Research Articles

Modifying Microstructure and Improving Mechanical Properties of New Ti-Al-V Titanium Alloys via Fe Addition

A comprehensive study was carried out to investigate the effects of Fe addition (0–0.9 wt.%) on the microstructure evolution and mechanical properties of Ti-6Al-4V alloys. The results indicate that Fe addition has a significant refinement effect on the microstructure of titanium alloys; specifically, 0.9 wt.% Fe addition can lead to a 47.37% decrease in the width of lamellar α. The modulus also decreases by 18.89% with the increase in the Fe content, being 91.40 GPa in Ti-6Al-4V-0.9Fe. And the microhardness and wear resistance are improved due to Fe addition. In addition, the constitutive equation of the Fe content and the elastic compliance coefficient were calculated, which can better describe the relationship between Fe addition and the elastic–plastic properties of titanium alloys. The slip systems’ activity during the deformation process was also discussed using the Schmid factor. It shows that Fe addition is beneficial for the activity of prismatic and pyramidal slip systems, especially in the {101¯0} <112¯0>, {101¯1} <112¯3>, and {112¯2} <112¯3> slip systems.

Titanium Alloy Ti-6Al-4V Electrochemical Dissolution Behavior in NaNO3 and NaCl Solutions at Low Current Density

Jet electrochemical micromilling (JEMM) exhibits significant potential for high-efficiency and high-quality machining of titanium alloy microstructures. However, during the JEMM process, the machined surface of the workpiece inevitably experiences stray current attacks at low current levels. Due to the formation of a dense passive film on the surface of the titanium alloy under electrochemical action, stray corrosion occurs on the machined surface. Hence, the electrochemical dissolution behavior of titanium alloys at low current densities directly impacts both machining efficiency and quality. This study first analyzed the effects of electrolyte composition and current density on the transpassive potential, breakdown of the passive film, current efficiency, and the dissolved surface on Ti-6Al-4V. The transpassive potential and electrochemical impedance of Ti-6Al-4V were found to be lower in NaCl solution than in NaNO3 solution. In addition, lower current densities enabled higher current efficiency and resulted in a more uniform and flat dissolution surface. Subsequent experiments used these two solutions for JEMM of complex-shaped microstructures on Ti-6Al-4V. The findings demonstrated that, compared to the NaNO3 solution, the use of NaCl solution increases the material removal rate by approximately 30%, enhances the aspect ratio by about 26%, and reduces surface roughness by roughly 58%. This indicates that employing NaCl solution can lead to superior machining efficiency and quality.

Laser Cutting of Titanium Alloy Plates: A Review of Processing, Microstructure, and Mechanical Properties

The growing use of titanium alloys has led to the gradual replacement of traditional processing methods by laser cutting technology, making it the preferred method for processing titanium alloy plates due to its high efficiency, precision, and adaptability. In this review, the characteristics of laser cutting technology and its application in titanium alloy plate processing are summarized, outlining several aspects of the cutting process, microstructure, and mechanical properties of the material after cutting, along with simulation predictions. Previous research categorized laser-cutting input parameters into beam parameters and process parameters, with the commonly used parameters being the laser power, cutting speed, and gas pressure. Various parameter combinations can achieve different cutting qualities, and seven indices can be used to evaluate the cutting process, with the surface roughness and slit width serving as the most common indices. Different auxiliary gases have shown a significant impact on the laser cutting quality, with commonly used gases consisting of nitrogen, argon, and air. Argon-assisted cutting generally results in better surface quality. Due to the rapid temperature change, the titanium alloy microstructure will undergo a non-diffusive martensitic phase transformation during laser cutting, producing a heat-affected zone. Experimental studies and simulations of the mechanical properties have shown that the occurrence of a martensitic phase transformation increases the hardness and residual tensile stress of the material, which reduces the fatigue strength and static tensile properties. In addition, studies have found that the more streaks appear on the cut surface, the lower the fatigue strength is, with fatigue cracks arising from the stripes. Hence, the established analytical solution model and three-dimensional finite element model can effectively predict the temperature distribution and residual stress during the cutting process. This can provide a better understanding of the high residual stress characteristics of the cutting edge and the stripe formation mechanism, allowing researchers to better explore the mechanism of laser cutting.

The synergistic effect of gradient nanostructure and residual stress induced by expansion deformation on the tension-tension fatigue performance of Ti6Al4V holes

A large number of titanium alloy structural components in aircraft are joined and assembled through holes. The complex service conditions pose high requirements for the fatigue performance of the holes. The study aims to use expansion deformation to induced gradient nanostructures and residual stress enhance the fatigue performance of Ti6Al4V holes. By studying the evolution process of the microstructure in titanium alloys, the formation mechanism of gradient nanostructures has been obtained, and their impact on various stages of fatigue fracture is analyzed. The results showed that the split sleeve cold expansion (CE) process caused severe plastic deformation on the surface of the hole, resulting in a large amount of LAB production and a transformation of the main texture components. Meanwhile, the gradient nanostructures are formed on the hole surface, which include nanocrystalline layer, superfine lath grain layer, lath grain layer and severe deformation layer. During the expansion deformation process, the slip of dislocations leads to the formation of high-density dislocation structures, which evolve into subgrain boundaries and then undergo dynamic recrystallization through subgrain rotation to achieve grain refinement. What's more, the degree of grain refinement, depth of gradient nanostructure, and residual stress increase as the expansion deformation intensifies. The depth of gradient nanostructure is 300 μm, and the maximum residual stress is −419 MPa, when the deformation is 5 %. The combined effect of gradient nanostructures and high residual compressive stress induced by expansion deformation has increased the total fatigue life and crack propagation life of CE-5 specimen by 2.9 and 3.4 times compared to NCE specimen. This work provides an effective method and theoretical analysis for improving the fatigue life of titanium alloy holes.

Achieving enhanced tensile strength-ductility synergy through phase modulation in additively manufactured titanium alloys

The quest for titanium alloys with an optimal combination of tensile strength and ductility is paramount for their application in critical industries. Additive manufacturing (AM) offers a unique avenue to control the alloy's microstructure, thereby modulating its mechanical properties. This study presents a comprehensive investigation into the phase modulation strategies in AM-processed titanium alloys to achieve a synergistic improvement in tensile strength and ductility. The advancements in this work have enabled the progressive evolution of titanium alloy microstructures with addition of β stable elements, transitioning from near α-Ti alloys to α+β alloys, and finally to near β-Ti alloys. This approach has led to a significant enhancement in the tensile strength of as-prepared titanium alloys, complemented by a moderate retention of ductility. The primary contributors to this mechanical property improvement are identified as solid solution strengthening, grain refining strengthening, and grain boundary strengthening, all of which are facilitated by strategic phase modulation. Moreover, the generation of refined α lamellas, equiaxed α phases, and discontinuous grain boundary α phases has been shown to be instrumental in promoting coordinated deformation within the alloy, thus enhancing the overall ductility. Varying fracture modes demonstrated the potential of microstructure tailoring via multi-eutectoid elements alloying, facilitating the in-depth understanding of crack initiation, propagation, and strain-to-failure, shedding the lights on the enhancement of strength-ductility synergy.

Simulation of ultrasonic characterization for the microstructure of titanium alloy

Simulation of ultrasonic characterization for the microstructure of titanium alloy

Microstructure and tensile properties of Ti6Al4V with layered structures fabricated by electron beam directed energy deposition

Adding modified elements to regulate the microstructure of titanium alloys prepared by additive manufacturing and enhance the mechanical properties of as-deposited titanium alloys has become a research focus. However, this approach also brings the risk of causing the materials with high strength-low plasticity characteristics. In this study, a macroscopic layered structure titanium alloy with hierarchical heterogeneous characteristics was constructed by alternately melting Ti6Al4V wire and Cu-modified Ti6Al4V wire using electron beam as a heat source. The tensile test results demonstrated that the layered structure titanium alloy exhibiting great strength and plasticity matching, which proved that the layered structure constructed via additive manufacturing could effectively alleviate the strength-ductility trade-off. The mechanism of synergistical improvement of the fracture strength and plastic was attributed to the deformation-induced strengthening as well as multiple crack-deflection and tip-passivation occurring during the plastic deformation of layered Ti6Al4V alloy.

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.