Pouya Fard, Ali
LOW UNCERTAINTY SURFACE AREA MEASUREMENT USING DEFLECTOMETRY
1 online resource (91 pages) : PDF
University of North Carolina at Charlotte
Deflectometry has been introduced as a viable alternative to interferometric measurement of specular freeform optical surfaces. Deflectometry measurements show high repeatability; however, due to the errors remaining after the calibration and data analysis, surface measurements have been limited to systematic micron-level biases in the low-spatial frequency limit. Through better modeling, calibration and data processing, the systematic errors remaining after calibration can be minimized, giving deflectometry the potential to be used as a precision metrology tool. We identify and quantify a large systematic phase bias in the deflectometry measurement that is introduced during the analysis of the measurement data. We show that this bias is a significant contributor to the well-known micron-level low spatial-frequency errors in deflectometry that are commonly attributed to calibration errors. To further minimize the systematic errors, a systematic calibration approach is developed and validated with simulation and experiment. The calibration approach is implemented on a deflectometry system and used to measure a specular flat and a spherical concave surface with a few hundreds of nanometers of uncertainty. A flexible method of geometrical modeling and calibration of the shape of deflectometry screens is also proposed. This method is used to model and calibrate the shape of a spherical screen in a simulated deflectometry measurement and used to experimentally measure the out-of-flatness of the surface of a deflectometry LCD screen. This work enables quantitative form metrology with deflectometry systems using complex screen geometries that are limited to qualitative surface defect inspection.
DEFLECTOMETRYOPTICAL METROLOGYPHASE MEASURING DEFLECTOMETRYPRECISION SURFACE METROLOGY
Optical Science & Engineering
Davies Allen, Angela
Evans, ChristopherFarahi, FaramarzGbur, Greg
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.
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