This paper details the feasibility of using fiber-based tools in a computer numerical control (CNC) environment to process optical materials and their ability to reduce the amplitude of pre-existing mid-spatial-frequency (MSF) surface errors. The work is motivated by earlier research conducted by the group exploring the ability of polymeric fiber-based tools to remove material from BK7 glass substrates. To evaluate these tools in a CNC environment, three tasks are explored. First, the ability of the tools to maintain their form and material removal profile while operating under translational conditions is explored. Second, the ability of the tools to disengage and re-engage with the workpiece edge, and how this affects the tool's material removal profile. Finite element (FE) modelling of the fiber-workpiece edge interaction was conducted to support the experimental work. And third, the deterministic behavior of the tool under full raster conditions is verified. Testing on a 3-axis CNC machine tool demonstrated that the tooling is sufficiently robust and stable to operate under translational and rotational speeds of 30 mm/s and 1000 rpm, respectively. Both the FE modeling and experimental testing confirmed the truncation of a fiber's material removal profile as a fiber extends beyond the workpiece edge. The ability of fiber-based tools to reduce MSF errors was explored both through FE modeling and experimental testing on germanium samples. Both the FE model and experimental results demonstrate that fiber-based tools can successfully reduce pre-existing MSF errors.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/AO.56.008266 | 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.
View Article and Find Full Text PDFRev Sci Instrum
October 2024
Commonwealth Fusion Systems, Devens, Massachusetts 01434, USA.
Recording and modulation of neuronal activity enables the study of brain function in health and disease. While translational neuroscience relies on electrical recording and modulation techniques, mechanistic studies in rodent models leverage genetic precision of optical methods, such as optogenetics and imaging of fluorescent indicators. In addition to electrical signal transduction, neurons produce and receive diverse chemical signals which motivate tools to probe and modulate neurochemistry.
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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!