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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.

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While benchmark datasets have been proposed for testing computer vision and 3D shape retrieval algorithms, no such datasets have yet been put forward to assess the relevance of these techniques for engineering problems. This paper presents several distinctive benchmark datasets for evaluating techniques for automated classification and retrieval of CAD objects. These datasets include (1) a dataset of CAD primitives (such as those common in constructive solid geometry modeling); (2) two datasets consisting of classes generated by minor topological variation; (3) two datasets of industrial CAD models classified based on object function and manufacturing process, respectively; (4) and a dataset of LEGO c models from the Mindstorms c robotics kits. Each model in the datasets is available in three formats -ACIS SAT, ISO STEP, and as a VRML mesh (some models are available under several different fidelity settings). These are all available through the National Design Repository.Using these datasets, we present comprehensive empirical results for nine (9) different shape and solid model matching and retrieval techniques. These experiments show, as expected, that the quality of precision-recall performance can significantly vary on different datasets. These experiments reveal that for certain object classes and classifications, such as those based on manufacturing processes, all existing techniques perform poorly. This study reveals the strengths and weaknesses of existing research in these areas, introduces open challenge problems, and provides meaningful datasets and metrics against which the success of current and future work can be measured.

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).

Within the past few years, there has been a steady, substantial growth of interest in “long-term” archiving of digital data. This problem is particularly acute in many branches of engineering design, where cycles of technological obsolescence in supporting tools happen much more rapidly than those of designed products. Capturing and preserving design knowledge through these cycles is a major challenge that has come to be recognized by many government, industry, and research organizations. The ability to do so has important operational, efficiency, and legal ramifications for the manufacturing industry and its customers. This paper describes this problem, presenting examples of both why it must be addressed and why it is a challenge. In particular it relates preservation of engineering data to digital archiving efforts in other domains as well as ongoing work within the engineering research community on design repositories. As is shown, long term archiving of digital design knowledge draws upon both but possesses its own unique issues. Much of this discussion is couched within the language of the ISO Open Archival Information Systems (OAIS) Reference Model, including a mapping from an existing significant design repository into the OAIS model. In this way, it is hoped that this paper will widen the discussion on digital archiving within the community of this conference as well as help connect to research in other areas.