Crystal Plasticity Model Validation Using Combined High-Energy Diffraction Microscopy Data for a Ti-7Al Specimen
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E of the great hopes in the materials community is to better link modeling and design work in order to both develop new engineering materials, as well as to use current or legacy materials in new applications. This TODD J. TURNER and PAUL A. SHADE, Materials Research Engineers, are with the Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433. Contact e-mail: [email protected] JOEL V. BERNIER, Staff Scientist, and SHIU FAI LI, formerly Staff Scientist, currently External Collaborator, are with the Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550. JAY C. SCHUREN, formerly Materials Research Engineer with the Materials and Manufacturing Directorate, Air Force Research Laboratory, is now Senior Director of Data Science with Nutonian Inc., Somerville, MA 02144. PETER KENESEI, Beamline Scientist, and JONATHAN ALMER, Group Leader, are with Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439. ROBERT M. SUTER, Professor, is with Carnegie Mellon University, Pittsburgh, PA 15213. Todd J. Turner and Paul A. Shade are employed by the Air Force Research Laboratory. U.S. Government work is not protected by U.S. copyright. Manuscript submitted May 24, 2016 Article published online November 18, 2016 METALLURGICAL AND MATERIALS TRANSACTIONS A
shift to an increased reliance on predictive models is often referred to as Integrated Computational Materials Engineering (ICME).[1] Currently, extensive experimental testing regimes are utilized to certify materials for any given application. This empirical approach generates large amounts of data, which is both time consuming and costly. While modeling is used extensively in engineering design, linking materials models that predict properties and performance across the component life cycle from processing to final insertion remains elusive. This is in large part due to a lack of data collected at the relevant length scales. For instance, how can we validate models that predict fatigue initiation or small crack growth when we do not have validated experimental data on the length scale of these phenomena? As a consequence, advanced computational methods are bypassed for large-scale testing programs. This inherently limits the insertion of novel materials or innovative designs due to time and fiscal constraints. High-Energy Diffraction Microscopy (HEDM) is an experimental technique that utilizes monochromatic synchrotron radiation to non-destructively probe the micro-mechanical state of a material and its microstructure during deformation.[2–11] For the dataset presented VOLUME 48A, FEBRUARY 2017—627
and utilized in this work, we combine near-field HEDM (nf-HEDM)[4–7] and far-field HEDM (ff-HEDM) measurements[8–11] which were concurrently collected. The nf-HEDM technique utilizes an area X-ray detector placed close to the specimen (approximately 5 mm) and provides spatially resolved orientation maps from which intragranular misorientation and grain morphology are calculated. This is used to p
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