Share Email Print

Proceedings Paper

Automated inspection of optical surface cleanliness (Conference Presentation)
Author(s): Daniel Kiefhaber; Peter Würtz; Fabian Etzold; Willi Maurer; Jean-Michel Asfour

Paper Abstract

Manual visual inspection is the standard method to evaluate cleanliness, scratches, and digs on optical components according to ISO 10110-7 or MIL-PRF-13830B. However, the limited optical resolution of the human eye turns the inspection of critical samples like high power optics or sensor cover glasses into a time-consuming and tiring task with inherently subjective results. Machine vision systems provide an objective alternative with reliable and reproducible results and additional advantages: Documentation in the form of inspection reports can be generated at no extra cost and data can be collected to analyze typical problems in the manufacturing process. Automated inspection systems need high resolution and well-designed illumination for the inspected part to ensure visibility of smallest imperfections with sizes as small as few micrometers. At the same time, short duty cycles of the inspection system are a key user requirement. Manual-visual is performed in dark field conditions. In automated inspection systems, a dark field optical setup is also advantageous, giving the best imperfection contrast. Generating good dark field conditions when inspecting a curved optical, i.e. specular, surface is not trivial. Using a multitude of illumination angles is necessary for good (and orientation-independent) imperfection visibility, but every additional illumination direction increases the risk of specular reflections into the camera, leading to blind spots in the images. We have developed two approaches to deal with this problem: In the first, reflections are avoided by design, in the second, reflections are treated in data processing. Our commercially available ARGOS system is using a line-scan camera in combination with a rotation stage. The light source is illuminating from almost every direction, except for the direction parallel to the line sensor of the camera itself. This gives excellent imperfection visibility while avoiding direct reflections and maintaining dark-field conditions on rotationally symmetric samples with almost arbitrary curvature. The second approach that has been successfully implemented is suitable also for matrix cameras: Multiple illumination configurations are used sequentially, each generating direct reflections in different portions of the image. These image-sets are then combined into a dark-field image without reflections, by applying suitable filter masks to remove specular reflections from the images. The ARGOS-approach with the line-scan camera has the additional advantage, that very high image resolution is possible by rotating the sample while observing a line aligned with the sample radius. In the standard version, image resolution is up to 250 mega pixels; in the high-resolution version up to 1 giga pixel is possible – in scan times of a few seconds. On the other hand, the requirement to rotate the sample is not ideal for an automated inspection of a batch of samples. In cases when the large resolution is not required, e.g. for smaller elements, using a matrix camera is more efficient, as the acquisition of a sufficient number of images with different illumination conditions takes only a fraction of a second, allowing for short overall inspection times.

Paper Details

Date Published: 22 July 2019
Proc. SPIE 11061, Automated Visual Inspection and Machine Vision III, 1106107 (22 July 2019); doi: 10.1117/12.2526321
Show Author Affiliations
Daniel Kiefhaber, Dioptic GmbH (Germany)
Peter Würtz, Dioptic GmbH (Germany)
Fabian Etzold, Dioptic GmbH (Germany)
Willi Maurer, Dioptic GmbH (Germany)
Jean-Michel Asfour, Dioptic GmbH (Germany)

Published in SPIE Proceedings Vol. 11061:
Automated Visual Inspection and Machine Vision III
Jürgen Beyerer; Fernando Puente León, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?