Deformation and optical aberration prediction in ultra-precision Single Point Diamond Turning of optical components
Year: 2024
Type: article
Abstract: Aluminum is the material of choice for the majority of aerospace components, and, in the past few years, its application has been extended also to the mirrors of space telescopes because of the improved thermal behavior and the possibility to build the entire telescope with the same material. However, the low elastic modulus of such material, combined with the extremely tight tolerances of optical applications, make the production of these components very challenging and, usually, based on a trial-and-error approach. This paper presents a structured methodology for the prediction of the results of manufacturing in Single Point Diamond Turning of optical components, both in terms of absolute deformation as well as optical aberrations (via Zernike polynomials). All the most significant parameters acting on the workpiece have been simulated and combined. The proposed approach has been experimental validated on an actual aluminum mirror, proving its good accuracy (<5 % rms error). While some improvement can be performed to better match the experimental data in terms of Zernike coefficients, especially for non-symmetric aberrations, this paper forms the basis for an off-machine optimization of the SPDT process, drastically reducing the trial-and-error efforts. • A methodology taking into account of gravity, centrifugal force, tool misalignment errors and fixturing in SPDT is proposed. • The proposed approach integrates the use of Zernike polynomials to analyze the results. • Experimental validation shows an accuracy of the model of <5 % rms error. • Capability to identify the most relevant optical aberrations and correctly predict the single most significant one.
Source: Precision Engineering
Institution: University of Florence
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,340
OpenAlex ID: W4403524273