Complex curved surface shape error evaluating method

Abstract

The invention discloses a complex curved surface shape error evaluating method. The method comprises performing interference detection on a complex curved surface to obtain surface shape data; secondly correcting the projection distortion of the surface shape data so that the spatial resolutions of the surface shape data in various direction can be consistent; thirdly, performing Zernike polynomial fitting on the corrected surface shape data to obtain low-frequency errors, according to a Zernike polynomial coefficient Zi, computing a low-frequency evaluating index of EL, wherein Ci represents the weight of every coefficient; fourthly, eliminating the low-frequency errors from the surface shape data, and then filtering out high-frequency errors through high-pass filtering to retaining intermediate-frequency errors; fifthly, evaluating the intermediate-frequency errors through a Slope RMS (root-mean-square) index; sixthly, computing the scattering rate of the high-frequency errors to obtain an evaluating index of the high-frequency errors; seventhly, if any of the three indexes above exceeds preset standards, determining that the surface shape errors of the complex curved surface are unqualified. The complex curved surface shape error evaluating method can effectively guide optical manufacturing production from the angle of imaging quality.

Description

technical field [0001] The invention belongs to the technical field of optical system detection and evaluation, and in particular relates to a complex surface shape error evaluation method. Background technique [0002] Aspheric surfaces and free-form surfaces are more and more widely used in optical systems, and the processing accuracy requirements are getting higher and higher. The surface shape requirements of the current high-resolution earth observation system have reached RMS of λ / 60 or even higher . Aspherical surfaces and free-form surfaces will gradually replace spherical surfaces in optical systems. [0003] However, aspheric and free-form surfaces will have manufacturing errors in optical processing, and these errors are parts that deviate from ideal aspheric surfaces. Traditional error characterization methods include error peak-to-valley (PV), root mean square (RMS) and waviness, etc., but these indicators are more or less derived from the concept of tradition...

AI Technical Summary

Problems solved by technology

The consequence of this problem is that optical manufacturing is produced according to some traditional indicators, and the obtained optical components are not necessarily the best imaging components. Sometimes in order to improve the performance of the imaging system, the manufacturing indicators have to be strictly required, resulting in over-processing. cost wasted

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