Optical inspection using multi-sensor multi-scale systems requires the selection of proper sensors, their parameters (e.g. resolution, N.A, lighting conditions), and measurement strategies. We propose an assistance system that automatically selects the suitable sensors and their parameters for an inspection specification. The specimen and the defects are described based on their properties (e.g. geometry, material etc) to the assistance system. The system then uses different "sub-assistants", each designed for a specific measurement technique, to recommend the most suitable measurement setups. The system and initial results for fringe projection techniques are presented.

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Optical inspection systems constitute hardware components (e.g. measurement sensors, lighting systems, positioning systems etc.) and software components (system calibration techniques, image processing algorithms for defect detection and classification, data fusion, etc.). Given an inspection task choosing the most suitable components is not a trivial process and requires expert knowledge. For multiscale measurement systems, the optimization of the measurement system is an unsolved problem even for human experts. In this contribution we propose two assistant systems (hardware assistant and software assistant), which help in choosing the most suitable components depending on the task considering the properties of the object (e.g. material, surface roughness, etc.) and the defects (e.g. defect types, dimensions, etc.). The hardware assistant system uses general rules of thumb, sensor models/simulations and stored expert knowledge to specify the sensors along with their parameters and the hierarchy (if necessary) in a multiscale measurement system. The software assistant system then simulates many measurements with all possible defect types for the chosen sensors. Artificial neural networks (ANN) are used for pre-selection and genetic algorithms are used for finer selection of the defect detection algorithms along with their optimized parameters. In this contribution we will show the general architecture of the assistant system and results obtained for the detection of typical defects on technical surfaces in the micro-scale using a multiscale measurement system.