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Alpha-Beta Titanium Alloys

The Experts below are selected from a list of 129 Experts worldwide ranked by ideXlab platform

S. L. Semiatin – 1st expert on this subject based on the ideXlab platform

  • Advances in the Development of Processing – Microstructure Relations for Titanium Alloys (Postprint)
    , 2016
    Co-Authors: S. L. Semiatin, A. L. Pilchak

    Abstract:

    Abstract : Advances in the fundamental understanding of microstructure evolution and plastic flow during primary and secondary processing of Titanium Alloys are summarized. Examples focus on the recrystallization of the beta phase and spheroidization of alpha lamellae during the breakdown of alpha/beta Titanium Alloys, challenges in the rolling of foil of alpha/beta and gamma-TiAl Alloys, and the effect of microstructure and composition on the superplastic flow behavior of alpha/beta Titanium Alloys. Particular attention is given to models describing the refinement (or coarsening) of microstructural scale and non-uniformities that may impact forming response during operations such as rolling of sheet and superplastic forming. Current understanding of the persistence of microstructural anomalies such as microtextured regions (aka macrozones) is also described. Finally, recent developments in the characterization of such features via microscopy and ultrasonics are discussed.

  • A coupled EBSD/EDS method to determine the primary- and secondary-alpha textures in Titanium Alloys with duplex microstructures
    Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2008
    Co-Authors: A.a. Salem, Michael G. Glavicic, S. L. Semiatin

    Abstract:

    Abstract A method for separating the textures of primary alpha (αp) and secondary alpha (αs) in alpha/beta Titanium Alloys with a duplex microstructure was developed. Utilizing electron backscatter diffraction (EBSD) and energy-dispersive spectroscopy (EDS), the approach relies on the non-uniform partitioning of alloying elements between primary alpha and regions containing secondary-alpha lamellae and residual beta matrix phase. The method was evaluated using samples of Ti–6Al–4V for which vanadium partitions strongly to secondary alpha + beta regions. The technique thus provides a useful tool for quantifying the evolution of deformation texture in the primary alpha and transformation texture in secondary alpha formed via decomposition of the beta matrix following hot working or final heat treatment.

  • low temperature coarsening and plastic flow behavior of an alpha beta Titanium billet material with an ultrafine microstructure
    Metallurgical and Materials Transactions A-physical Metallurgy and Materials Science, 2008
    Co-Authors: G A Sargent, A P Zane, P N Fagin, A K Ghosh, S. L. Semiatin

    Abstract:

    The influence of microstructure evolution on the low-temperature superplasticity of ultrafine alpha/beta Titanium Alloys was established. For this purpose, the static and dynamic coarsening response and plastic flow behavior of Ti-6Al-4V with a submicrocrystalline microstructure were determined via a series of heat treatments and uniaxaial compression tests at temperatures of 650 °C, 775 °C, and 815 °C. At all test temperatures, static coarsening exhibited diffusion-controlled (r 3 vs time) kinetics and followed a dependence on phase composition and volume fraction qualitatively similar to previous observations at 850 °C to 950 °C. Dynamic coarsening at 775 °C and 815 °C and strain rates of 10−4 and 10−3 s−1 were similar to prior higher-temperature observations as well in that the kinetics were approximately one order of magnitude faster than the corresponding static behaviors. The increase in coarsening rate with superimposed deformation was attributed to the enhancement of diffusion by dislocations generated in the softer beta phase. With respect to deformation response, plastic flow was superplastic with m values of ∼0.6 at 650 °C, 775 °C, and 815 °C and strain rates of 10−4 and 10−3 s−1. Dynamic coarsening resulted in flow hardening at both temperatures and strain rates for a short preheat time (15 minutes) but was noticeably reduced when a longer preheat time (1 hour) was used prior to testing at 10−3 s−1. The latter behavior was largely attributed to noticeable static coarsening during preheating. A generalized constitutive relation based on a single stress exponent and the instantaneous alpha particle size was shown to describe the superplastic flow of ultrafine Ti-6Al-4V at low and high temperatures.

N. L. Richards – 2nd expert on this subject based on the ideXlab platform

  • Quantitative evaluation of fracture toughness-microstructural relationships in Alpha-Beta Titanium Alloys
    Journal of Materials Engineering and Performance, 2004
    Co-Authors: N. L. Richards

    Abstract:

    The fracture toughness of two Alpha-Beta Titanium Alloys containing an alpha platelet in a transformed beta matrix has been examined in terms of the microstructural parameters controlling the fracture initiation and propagation in the Alloys. Equations have been formulated that show that the highest toughness values of both Alloys were associated with the finest platelet spacings and the thickest alpha platelets. It is proposed that the fracture initiation process in both Alloys is controlled by the distance between the platelets, the fracture toughness of the Alloys being dependent on the distance between active centers of void nucleation, i.e., as a function of the alpha platelet thickness and spacing between the platelets. Seven models of ductile fracture relating fracture toughness to mechanical property and microstructural parameters have been compared in their ability to predict the toughness of the Alloys after solution treatments, which produce varying platelet thickness and inter-platelet spacings. The principle has been adopted following Rice and Rosengren and Hutchinson (HRR)^[1,2] that there must be a 1/ x energy singularity at the crack tip, which also prescribes the stress and strain distribution ahead of a crack tip. Any model not incorporating these requirements should be rejected.

Daniel P. Satko – 3rd expert on this subject based on the ideXlab platform

  • Microstructure-Informed Cloud Computing for Interoperability of Materials Databases and Computational Models: Microtextured Regions in Ti Alloys
    Integrating Materials and Manufacturing Innovation, 2017
    Co-Authors: Ayman A. Salem, Joshua B. Shaffer, Richard A. Kublik, Luke A. Wuertemberger, Daniel P. Satko

    Abstract:

    With the fast global adoption of the Materials Genome Initiative (MGI), scientists and engineers are faced with the need to conduct sophisticated data analytics on large datasets to extract knowledge that can be used in modeling the behavior of materials. This raises a new problem for materials scientists: how to create and foster interoperability and share developed software tools and generated datasets. A microstructure-informed cloud-based platform (MiCloud™) has been developed that addresses this need, enabling users to easily access and insert microstructure informatics into computational tools that predict performance of engineering products by accounting for microstructural dependencies on manufacturing provenance. The platform extracts information from microstructure data by employing algorithms including signal processing, machine learning, pattern recognition, computer vision, predictive analytics, uncertainty quantification, and data visualization. The interoperability capabilities of MiCloud and its various web-based applications are demonstrated in this case study by analyzing Ti6AlV4 microstructure data via automatic identification of various features of interest and quantifying its characteristics that are used in extracting correlations and causations for the associated mechanical behavior (e.g., yield strength, cold-dwell debit, etc.). The data were recorded by two methods: (1) backscattered electron (BSE) imaging for extracting spatial and morphological information about alpha and beta phases and (2) electron backscatter diffraction (EBSD) for extracting spatial, crystallographic, and morphological information about microtextured regions (MTRs) of the alpha phase. Extracting reliable knowledge from generated information requires data analytics of a large amount of multiscale microstructure data which necessitates the development of efficient algorithms (and the associated software tools) for data recording, analysis, and visualization. The interoperability of these tools and superior effectiveness of the cloud computing approach are validated by featuring several examples of its use in alpha/beta Titanium Alloys and Ni-based superAlloys, reflecting the anticipated computational cost and time savings via the use of web-based applications in implementations of microstructure-informed integrated computational materials engineering (ICME).

  • Microstructure-Informed Cloud Computing for Interoperability of Materials Databases and Computational Models: Microtextured Regions in Ti Alloys
    Integrating Materials and Manufacturing Innovation, 2017
    Co-Authors: Ayman A. Salem, Joshua B. Shaffer, Richard A. Kublik, Luke A. Wuertemberger, Daniel P. Satko

    Abstract:

    With the fast global adoption of the Materials Genome Initiative (MGI), scientists and engineers are faced with the need to conduct sophisticated data analytics on large datasets to extract knowledge that can be used in modeling the behavior of materials. This raises a new problem for materials scientists: how to create and foster interoperability and share developed software tools and generated datasets. A microstructure-informed cloud-based platform (MiCloudTrade) has been developed that addresses this need, enabling users to easily access and insert microstructure informatics into computational tools that predict performance of engineering products by accounting for microstructural dependencies on manufacturing provenance. The platform extracts information from microstructure data by employing algorithms including signal processing, machine learning, pattern recognition, computer vision, predictive analytics, uncertainty quantification, and data visualization. The interoperability capabilities of MiCloud and its various web-based applications are demonstrated in this case study by analyzing Ti6AlV4 microstructure data via automatic identification of various features of interest and quantifying its characteristics that are used in extracting correlations and causations for the associated mechanical behavior (e.g., yield strength, cold-dwell debit, etc.). The data were recorded by two methods: (1) backscattered electron (BSE) imaging for extracting spatial and morphological information about alpha and beta phases and (2) electron backscatter diffraction (EBSD) for extracting spatial, crystallographic, and morphological information about microtextured regions (MTRs) of the alpha phase. Extracting reliable knowledge from generated information requires data analytics of a large amount of multiscale microstructure data which necessitates the development of efficient algorithms (and the associated software tools) for data recording, analysis, and visualization. The interoperability of these tools and superior effectiveness of the cloud computing approach are validated by featuring several examples of its use in alpha/beta Titanium Alloys and Ni-based superAlloys, reflecting the anticipated computational cost and time savings via the use of web-based applications in implementations of microstructure-informed integrated computational materials engineering (ICME).