Optical surfaces are subject to strict regulations regarding particles, long imperfections
(scratches), localised imperfections (wipers, holes, tool marks, coating defects)
and edge chips. Many of these requirements are summarised in the ISO 10110-7 standard.
Often, in addition to the standardised classifications, there are also customer-specific requirements such as drawing marks, particles, coating defects and other defects that cannot be cleaned (saw marks, cracks). In this case, a simple and expandable integration of these specifications into surface inspection is necessary.
Available inspection systems have the disadvantage that the adaptation to new products is often associated with great effort. Furthermore, the reproducibility and repeatability of the classification is not always guaranteed.
Neural networks can be adapted to the different requirements if the customer wishes so or
if an already available network is not optimally suited. In this way a maximum of performance,
expandability and accuracy is achieved.
Due to the fast and reliable classification we were able to halve the evaluation time and consequently double the output.
Many optical evaluation methods use classical image processing algorithms for classification and evaluation. In most cases, product adaptation requires a revision and extension of the algorithms.