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    NachhaltigAngewandte Naturwissenschaften und WirtschaftsingenieurwesenIPH Teisnach

    Beitrag (Sammelband oder Tagungsband)

    Gerald Fütterer, Simon Wittl, Lucas Bauer, Michael Wagner

    Alignment and thermal drift aspects of a four-tilted-mirror student project telescope

    Proceedings of SPIE 11171 (Sixth European Seminar on Precision Optics Manufacturing, 1117101 [April 9th-10th 2019, Teisnach]), Bellingham, WA, USA


    DOI: 10.1117/12.2530076

    Abstract anzeigen

    The Deggendorf Institute of Technology (DIT) and its Faculty of Applied Natural Sciences and Industrial engineering transfer a broad spectrum of knowledge to the students. The clarification of the interrelations that exist between seemingly isolated fields of knowledge is a permanent process. In order to put this into practice, a telescope construction project was started. The base of the in-house student project is the Technology Campus in Teisnach, which bundles capacities for process development, production and measurement of high-precision optics, including telescope optics. A first optical design, which is based on a subset of the parameter space published in 1989 by M. Brunn1, 2 (later built by D. Stevick as f/12-system3 ), made use of a primary mirror M1 with a diameter of 400 mm. An f/8-system provide a Strehl ratio SR ≥ 0.8 over an entire field of view of 0.7° deg. Even if this seems to be sufficient, manufacturing tolerances, adjustment tolerances, thermal drift and positional changes considerably reduce the Strehl ratio. In order to obtain reliable values of acceptable tolerances, statistical Monte Carlo analyses had been carried out. As consequences, the tube design was changed and the design of new mirror mounts started. This was done to achieve the required stiffness. The new tube designs, one based on carbon-fiber-reinforced polymer (CFRP) and one based on FeNi36, had been tested by using FEM analysis. In addition, the practicability of deep learning based aberration detection was tested. Zernike polynomials obtained by analyzing the star images with a Convolutional Neuronal Network (CNN). The current state of the development is described.