Publications

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This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removes a thin layer of material and images the exposed surface. The images are then used to gain insight into the material properties and often to construct a 3-dimensional reconstruction of the material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed R2R controller will automate this process while improving the precision of the material removal. The proposed R2R controller solves an optimization problem designed to minimize the variance of the material removal subject to achieving the expected target removal. This optimization problem was embedded in an R2R framework to provide iterative feedback for disturbance rejection and convergence to the target removal amount. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our R2R controller from historical data. The proposed R2R controller is demonstrated through simulations. Future work will empirically demonstrate the proposed R2R through experiments with a real MSS system.

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Streams of multimodal three-dimensional (3D) and four-dimensional (4D) data are revolutionizing our ability to design and predict the behavior of a broad array of advanced materials systems. Over the last 10 years, a new 3D imaging platform consisting of a femtosecond (fs) pulsed laser coupled with a focused ion beam scanning electron microscope (FIB SEM) has been developed by UC Santa Barbara in collaboration with Thermo Fisher Scientific (formerly FEI). The femtosecond-laser-enabled FIB SEM, called the TriBeam, has become one of the only 3D serial sectioning methods available that can gather millimeter-scaled multimodal datasets at sub-μm voxel resolutions; these length scales are critical for many materials problems. Multimodal chemical, crystallographic, and morphological information can be gathered rapidly on a layer-by-layer basis and reconstructed in 3D. Large (gigabyte to terabyte scale) 3D datasets have been generated for a broad array of materials systems, including metallic alloys, ceramics, biomaterials, polymer- and ceramic-matrix composites, and semiconductors. The research tasks performed have resulted in a completely new design, operating with a dual-wavelength femtosecond-pulsed laser on a plasma focused ion beam (PFIB) platform.

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The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

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