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.
Plumley, John B.; Cook, Adam W.; Larsen, Christopher A.; Artyushkova, Kateryna; Han, Sang M.; Peng, Thomas L.; Kemp, Richard A.
Here, the transparent electric conductors made of indium tin oxide (ITO)-doped glass prepared by a flash lamp annealing (FLA) process were compared with ITO-doped glass prepared via a conventional rapid thermal annealing (RTA) process. Stylus surface profilometry was used to determine thicknesses, scanning electron microscopy was used to image surfaces, X-ray diffraction was used to determine film structures, X-ray photoelectron spectroscopy was used to determine oxidation states and film compositions, 4-point probe measurements were used to determine electrical conductivities, UV–Vis spectroscopy was used to determine film transparencies, and selective light filtering was used to determine which wavelengths of light are needed to anneal ITO into a visibly transparent electrically conductive thin film via an FLA process. The results showed that FLA with visible light can be used to nearly instantaneously anneal ITO to create visibly transparent and electrically conductive ITO thin films on glass. The FLA process achieved this by predominately exciting unoxidized indium, unoxidized tin, tin monoxide (SnO), and non-stoichiometric indium oxide (InO x ), appropriately distributed in an electron beam physical vapor-deposited amorphous ITO thin film, to allow their oxidation and crystallization into an electrically conductive visibly transparent ITO. Though it is possible to prepare ITO-doped glass that is more transparent with an RTA process, the FLA process is significantly faster, has comparable electrical conductivity, and can strongly localize heating to areas of the as-deposited ITO thin film that are not electrically conductive and visibly transparent.
Additive manufacturing offers unprecedented opportunities to design complex structures optimized for performance envelopes inaccessible under conventional manufacturing constraints. Additive processes also promote realization of engineered materials with microstructures and properties that are impossible via traditional synthesis techniques. Enthused by these capabilities, optimization design tools have experienced a recent revival. The current capabilities of additive processes and optimization tools are summarized briefly, while an emerging opportunity is discussed to achieve a holistic design paradigm whereby computational tools are integrated with stochastic process and material awareness to enable the concurrent optimization of design topologies, material constructs and fabrication processes.
