Polymeric pad or pitch-based tools combined with loose abrasive slurries are typically used in the polishing of optical materials. In this paper, the potential of fiber-based tools to both remove material and provide high quality surface finishes on BK7 glass is explored. The potential advantage of fiber-based tools over traditional tools is their inherent compliance, which could accommodate varying workpiece surface curvatures as found in aspheres and freeforms. To evaluate the new tools, both experimental and finite element (FE) modeling approaches were taken. A FE model consisting of a single fiber engaged with the workpiece surface was used to estimate the shape and magnitude of the pressure distribution exerted by the fiber on the workpiece surface. Two different tool configurations, yielding two different Fes, predicted pressure distributions, were used to polish BK7 samples, and the material removal profiles were interferometrically measured. The resulting profiles and the predicted pressure distributions share the same v-shape. While differences in scale exist between the experimental and FE-predicted profiles, the tool generating higher material removal had the greater predicted pressure distribution, thus demonstrating the ability of the FE model to provide insights into tool design. Additional testing was conducted to determine if the tool's removal rate can be predicted by Preston's equation. Initial results indicate the equation is valid within the range of parameters tested. The surface roughness of BK7 samples processed by this tool was measured and some deterioration on the Sq value was noted; the surface roughness increased from 1.89 to 3.66 nm Sq. Over several hours of continuous use, the load applied by the fibers decays in a repeatable manner, and little wear was observed on the fibers after 5.33 h of polishing.
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http://dx.doi.org/10.1364/AO.55.004307 | DOI Listing |
Achieving high-fidelity image transmission through turbid media is a significant challenge facing both the AI and photonic/optical communities. While this capability holds promise for a variety of applications, including data transfer, neural endoscopy, and multi-mode optical fiber-based imaging, conventional deep learning methods struggle to capture the nuances of light propagation, leading to weak generalization and limited reconstruction performance. To address this limitation, we investigated the non-locality present in the reconstructed images and discovered that conventional deep learning methods rely on specific features extracted from the training dataset rather than meticulously reconstructing each pixel.
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View Article and Find Full Text PDFBiomed Opt Express
June 2024
Biophotonics Platform, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal.
Advancements in optical imaging techniques have revolutionized the field of biomedical research, allowing for the comprehensive characterization of tissues and their underlying biological processes. Yet, there is still a lack of tools to provide quantitative and objective characterization of tissues that can aid clinical assessment in vivo to enhance diagnostic and therapeutic interventions. Here, we present a clinically viable fiber-based imaging system combining time-resolved spectrofluorimetry and reflectance spectroscopy to achieve fast multiparametric macroscopic characterization of tissues.
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