More than 95% of industrial inspection systems still rely on pure 2D information. Different measurement tasks like defect detection, micro-structure characterization, spectral characterization and dimensional metrology are typical application scenarios where standard 2D image processing based measurement systems are used. For dimensional measurements typically edges are used as the primary features for measuring lengths and positions. The accuracy of the corresponding (sub-pixel) edge position measurements is fundamentally limited by photon noise, discretization noise and electronic (camera) noise. For some applications, photon noise and electronic noise can be reduced by temporal averaging. We propose a very simple and cheap modification to improve the accuracy of such edge-based measurement systems. All relevant noise contributions are reduced by using a computer-generated hologram within or in front of the imaging system. The hologram replicates the original image and leads to multiple copies of the image on the image sensor. Therefore, spatial averaging (instead of temporal averaging) can be used to reduce all mentioned statistical measurement uncertainties (including the main limitation, namely discretization), thereby increasing precision. We present the measurement setup and methodology, limiting factors and first results that show the capability to reach accuracies in the range of thousands of a pixel.
In this contribution we introduce an imaging based measurement setup, that is able to very accurately measure 3D relative positions between moving light sources. The system consists of two highspeed cameras, one equipped with a telecentric, the other with an endocentric lens. To improve accuracy of image based position detection, each lens is upgraded with a computer-generated-hologram (CGH) to replicate a single object point into a predefined pattern of spots on image plane. By averaging the centers of all replications, noise and other error contributions can be reduced. We will show how to apply image processing using two different approaches. The first approach is based on a tracking algorithm running on CPU reaching 330 fps. The second is a FPGA implementation to process whole images with a speed of 390 fps. Furthermore, we will demonstrate how threedimensional calibration can be done using the Nanomeasurement and Nanopositioning Machine NPMM-200. For the calibration, a three-dimensional multivariate polynomial is used. The standard deviations of residual error in object space for a calibration in a volume of 100 mm × 100 mm × 24 mm are σx = 0.367 μm, σy = 0.373 μm and σz = 0.437 μm (polynomial order = 9).
Artificial adaptive structures are systems which react on different environmental conditions. Bridges may dampen oscillations caused by heavy wind load, while high buildings may react to static loads from snow or much more dynamic ones, such as earthquakes. To interact, adaptive systems need control systems which control the actuators by measuring sensory input parameters (length, stress, deformation etc.). We realize a system where the sensory input comes from an image based optical camera system feeding the control system. We show first results for a holographic multipoint-based system for obtaining fast and highly accurate position measurement / deformation analysis with high accuracy at multiple spatial positions.
This manuscript gives an overview of the multipoint method and the development of an easy to use measurement system which measures the deformation of large buildings. The multipoint method has previously been tested1, 2 and used mainly under controlled laboratory conditions (e.g. indoors). Difficulties are introduced when this method is used outdoors, mainly because of the increased measurement scale and the uncontrolled environment. Differences in air pressure due to convection or wind, as well as fog or rain can cause severe perturbations to the light which propagates towards the sensors. Compared with existing systems, such as laser trackers, our system does not need to scan the building, which leads to much higher temporal resolution, which in turn can be used to achieve a reduced statistical measurement uncertainty (averaging in time).
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