We experimentally show that the thermal conductance across confined solid-solution crystalline thin films between parent materials does not necessarily lead to an increase in thermal resistances across the thin-film geometries with increasing film thicknesses, which is counterintuitive to the notion that adding a material serves to increase the total thermal resistance. Confined thin epitaxial Ca0.5Sr0.5TiO3 solid-solution films with systematically varying thicknesses in between two parent perovskite materials of calcium titanate and (001)-oriented strontium titanate are grown, and thermoreflectance techniques are used to accurately measure the thermal boundary conductance across the confined solid-solution films, showing that the thermal resistance does not substantially increase with the addition of solid-solution films with increasing thicknesses from μ1 to μ10 nm. Contrary to the macroscopic understanding of thermal transport where adding more material along the heat propagation direction leads to larger thermal resistances, our results potentially offer experimental support to the computationally predicted concept of vibrational matching across interfaces. This concept is based on the fact that a better match in the available heat-carrying vibrations due to an interfacial layer can lead to lower thermal boundary resistances, thus leading to an enhancement in thermal boundary conductance across interfaces driven by the addition of a thin "vibrational bridge"layer between two solids.
Advancements in computer technology have enabled three-dimensional (3D) reconstruction, data-stitching, and manipulation of 3D data obtained on X-ray imaging systems such as micro-computed tomography (μ-CT). Likewise, intuitive evaluation of these 3D datasets can be enhanced by recent advances in virtual reality (VR) hardware and software. Additionally, the generation, viewing, and manipulation of 3D X-ray diffraction datasets, such as pole figures employed for texture analysis, can also benefit from these advanced visualization techniques. We present newly-developed protocols for porting 3D data (as TIFF-stacks) into a Unity gaming software platform so that data may be toured, manipulated, and evaluated within a more-intuitive VR environment through the use of game-like controls and 3D headsets. We demonstrate this capability by rendering μ-CT data of a polymer dogbone test bar at various stages of in situ mechanical strain. An additional experiment is presented showing 3D XRD data collected on an aluminum test block with vias. These 3D XRD data for texture analysis (χ, φ, 2θ dimensions) enables the viewer to visually inspect 3D pole figures and detect the presence or absence of in-plane residual macrostrain. These two examples serve to illustrate the benefits of this new methodology for multidimensional analysis.
This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.
This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processe s for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.