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The near alpha titanium alloy, Ti-6424S, is utilized in manycritical high-temperature aerospace components due to its unique properties. However, oxygen ingress during elevated-temperature exposure induces formation of a subsurface brittle oxygen-rich layer (ORL), resulting in a deterioration of mechanical performance. This paper, for the first time, establishes the effect of the underlying microstructure on the formation and evolution of the ORL inα/β titanium alloys.In addition, models were developed to predict (i) the evolution of ORL as a function of the material microstructure, (ii) the effect of ORL on the critical strain for in-service crack initiation, and (iii) estimates of fatigue life of components made from a specific microstructure during in-service high temperature exposure and formation of ORL. In particular, five different microstructures wereproducedby tailored heat-treatments and thermally exposed at 650°C up to 420 hrs. The base metal and the ORL were quantified using microhardness indentations, optical microscopy, and scanning electron microscopy (electron backscatter diffraction (EBSD), backscattered electron (BSE), and secondary electron (SE) imaging). The effective diffusion coefficients (D eff) for each microstructure were calculated and then integrated into a critical strain model to predict crack initiation strain as a function of exposure time. The predicted ORL thickness was used to estimate fatigue life using experimentally measured crack growth data.The largest D eff coefficient was observed in a colony microstructure, whilea basketweave microstructure showed the smallestD eff. For several bimodal microstructures, D eff was noted to increase with increasing area fraction of secondary alpha colonies.

In this paper, a generalized workflow is outlined for the necessary integration of multimodal measurements and multiphysics models at multiple hierarchical length scales demanded by an Integrated Computational Materials Engineering (ICME) approach to accelerated materials development. Recognizing that multiple choices or techniques are typically available in each of the main steps, several exemplary analyses are detailed utilizing mainly the alpha/beta titanium alloys as an illustrative case. It is anticipated that the use and further refinement of these workflows will promote transparency and engender intimate collaborations between materials experts and manufacturing/design specialists by providing an understanding of the various mesoscale heterogeneities that develop naturally in the workpiece as a direct consequence of the inherent heterogeneity imposed by the manufacturing history (i.e., different thermomechanical histories at different locations in the sample). More specifically, this article focuses on three main areas: (i) data science protocols for efficient analysis of large microstructure datasets (e.g., cluster analysis), (ii) protocols for extracting reduced descriptions of salient microstructure features for insertion into simulations (e.g., regions of homogeneity), and (iii) protocols for direct and efficient linking of materials models/databases into process/performance simulation codes (e.g., crystal plasticity finite element method).

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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).

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