Paper
23 May 2011 Assistant systems for efficient multiscale measurement and inspection
Author Affiliations +
Abstract
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Avinash Burla, Tobias Haist, Wolfram Lyda, Mohamed Hassanine Aissa, and Wolfgang Osten "Assistant systems for efficient multiscale measurement and inspection", Proc. SPIE 8082, Optical Measurement Systems for Industrial Inspection VII, 808202 (23 May 2011); https://doi.org/10.1117/12.889344
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Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Inspection

Defect detection

Image processing

Genetic algorithms

Binary data

Genetics

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