Application of the NANOMEFOS Non-contact Measurement Machine in Asphere and Freeform Optics Production

Henselmans, R.; Gubbels, G.; Drunen, C. van

doi:10.1364/oft.2010.jmb47p

Cited by 5 publications

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“…Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Comparing the measurements of selected asphere and freeform surfaces is important in order to allow the performance of these measuring systems to be assessed.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, many measurement systems exist that use different measurement principles. For example, certain instruments apply optical [6][7][8][9] or tactile [4][5][6] point sensors that allow different scanning paths on the surface to be used. Often, spiral scans [7], circular scans [2] or line scans [4,5] are used, depending on the construction of the machine and on the sensor paths chosen by the customer.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of form measurement results for optical aspheres and freeform surfaces

Fortmeier

Schulz

2022

Meas. Sci. Technol.

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Comparing form measurement data for aspheres and freeform surfaces is an important tool for ensuring the quality and functionality of the devices used to take such measurements and may also allow the underlying measurement methods to be evaluated. However, comparing the highly accurate form measurements of such complex surfaces is a demanding task. It is diﬃcult to analyze measurement results whose accuracies are in the range of several tens of nanometers root-mean-square, especially when comparing data with diﬀerent, and anisotropic distributions of the 3D measurement points on the surface under test. In this paper, we investigate eight diﬀerent 3D measurement point distributions that are typical of highly accurate measurement systems currently in use and demonstrate the eﬀects of these distributions on the comparison results by using virtually generated data and applying diﬀerent evaluation strategies. The results show that, for the examples investigated, the diﬀerent 3D measurement point distributions can yield diﬀerent levels of accuracy for the comparison. Furthermore, an improved evaluation procedure is proposed and recommendations on how to signiﬁcantly reduce the inﬂuence of the diﬀerent 3D measurement point distributions on the comparison result are given. A method of employing virtually generated test data is presented that may be generalized in order to further improve and validate future comparison methods.

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