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Towards Surface Inference in Industrial Inspection

: Mohammadikaji, M.

Postprint urn:nbn:de:0011-n-4618256 (1.6 MByte PDF)
MD5 Fingerprint: d7b169485b8436c9fdef2ee82d1ff95e
Created on: 24.8.2017

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2016. Proceedings : Triberg-Nussbach, July, 24 to 29, 2016
Karlsruhe: KIT Scientific Publishing, 2017 (Karlsruher Schriften zur Anthropomatik 33)
ISBN: 978-3-7315-0678-2
DOI: 10.5445/KSP/1000070009
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2016, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Automated product inspection play an important role in today’s manufacturing process, and therefore, the design of optimized and precise measurement setups are a requirement for efficient product quality assurance. Due to the high dimensionality of the design space, a manual choice of the geometrical and optical parameters is associated with high costs, tedious experimental work, and often non-optimal results. Thus, automatic planning methods which seek to optimize the setup degrees of freedom for a particular measurement are of special importance in this field. For automatic evaluation of an inspection, there exist typical evaluation metrics including but not limited to, the measurement uncertainty and the scan resolution.
However, it is often not trivial how to combine different optimization
criteria to optimize the setup based on the requirements. For example, it is
not obvious how to compare an the result of an inspection with a high lateral
resolution and a high uncertainty against another inspection, with a low
lateral resolution but precise measurements. We propose to fuse the metrics
through a probabilistic surface inference to quantify the amount of information gained by a specific setup configuration. To this end, we model the surface by a Gaussian random process and introduce a local surface inference method based on the local surface orientation. The measurement points delivered by a laser triangulation setup are simulated using real-time graphical simulations, and the uncertainty of single point measurements are estimated. This data is further used as input to the local inference method. The inference results are demonstrated for the inspection of the intake manifold of a cylinder head.