In recent years image-processing has become a central part of optical inspection and measurement systems. Typically, after measuring the given specimen by utilizing a suitable sensor, image-processing algorithms are used to detect dedicated features such as surface defects. These algorithms are usually designed, optimized, and tested by an image-processing expert according to the task specifications. A methodology (based on genetic programming) is presented to automatically generate, optimize, and test such algorithms without the necessity of an image-processing expert. We also present several examples of inspection tasks to support the concept. For efficiency, an automated multi-scale multi-sensor inspection strategy is employed.
Multi scale systems offer the opportunity to balance the conflict between execution time, measurement volume and resolution
for the inspection of highly complex surface profiles. An example of such a task is the inspection of gears. At first,
the coarse position and form of the specimen is registered by a sensor measuring with comparatively low resolution but a
large field of view. Possible defects near to the resolution limit are indicated and new regions of interest for higher resolved
measurements are identified. As prerequisite for a successful multi-scale inspection, every sampled data set, acquired
in different scales and at varying positions, must be registered in one global data model. This is only possible if
the extrinsic coordinate transform from the sensor's internal coordinate system to the common, global coordinate system
of the inspected object and its uncertainties are known. In this paper, we present an approach for the extrinsic calibration
using the example of a multi-zoom fringe projection sensor mounted on a multi-axes measurement system. Finally we
show the measurement result of a gear, where several sampled patches are merged together into one point cloud with the
aid of the presented calibration.
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.
Efficient inspection of an object for deformations and defects requires comparison with an existing real or simulated reference model. Fourier descriptor (FDs) based shape analysis is an effective method for describing a shape using the Fourier transform. This shape representation can be easily modified to achieve shift, rotation, and scale invariance. We propose two new methods, namely the ring sampling and the spiral sampling methods, which enable the usage of FDs in order to detect defects on micro-optical elements like microlens arrays. As an example the measurement data obtained from a confocal microscope has been used to show the effectiveness of the two approaches for both indicating and detecting surface defects. Microlens arrays with different types of defects including global (deformed lenses causing aberrations) and local defects (scratches) were simulated using a confocal microscopy simulation tool to test the reliability of the methods. A classifier differentiates between global and local defective lenses. In order to represent other kinds of objects using FDs, the methods can be easily modified or extended. The whole process has been implemented into an automated multiscale multisensor measurement system, which focuses on fast detection of defects on micro-optical and microelectromechanical systems.
Surface metrology of MEMS requires high resolution sensors due to their fine structures. An automated multiscale
measurement system with multiple sensors at multiple scales enables fast acquisition of the surface data by utilizing high
resolution sensors only at locations required. We propose a technique that depends on the fact that often MEMS have
features (e.g. combs) repeating across the surface. These features can be segmented and fused to generate an ideal
template. We present an automated similarity search approach based on feature detection, rotation invariant matching,
and sum of absolute differences to find similar structures on the specimen. Then, similar segments are fused and replaced
in the original image to generate an ideal template.
For children, playing and learning is often one thing. They learn while playing and by playing the right games
they learn a lot. It is therefore obvious that we should use (among other things) games in order to fascinate
children for optics and to teach them the basic laws of optics. In this contribution we will introduce different
optical games for children in preschool and elementary school. The majority of commercial learning games on
the market do not achieve the ambitious goal of leading to fun and knowledge since very often there are serious
design flaws within these games. We introduce ten design rules for learning games that will enable you to create
your own successful learning game for a special topic. Exemplary, we will show games based on and for color
mixing and polarization.
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.
In former publications we presented an automated multiscale measurement system (AMMS) based on an adaptable
active exploration strategy. The system is armed with several sensors linked by indicator algorithms to identify
unresolved defects and to trigger finer resolved measurements. The advantage of this strategy in comparison to single
sensor approaches is its high flexibility which is used to balance the conflict between measurement range, resolution and
duration. For an initial proof of principle we used the system for inspection of microlens arrays.
An even higher challenge for inspection systems are modern micro electro-mechanical systems (MEMS). MEMS consist
of critical functional components which range from several millimeters down to micrometers and typically have
tolerances in sub-micron scale. This contribution is focused on the inspection of MEMS using the example of micro
calibration devices. This new class of objects has completely different surface characteristics and features hence it is
necessary to adapted the components of the AMMS. Typical defects found on calibration devices are for example broken
actuator combs and springs, surface cracks or missing features. These defects have less influence on the optical
properties of the surface and the MEMS surface generates more complex intensity distributions in comparison
microlense arrays. At the same time, the surface features of the MEMS have a higher variety and less periodicity which
reduce the performance of currently used algorithms. To meet these requirements, we present new indicator algorithms
for the automated analysis of confocal as well as conventional imaging data and show initial multiscale inspection
Multi-scale measurement systems utilise multiple sensors which differ in resolution and measurement field to pursue an
active exploration strategy. The different sensor scales are linked by indicator algorithms for further measurement
initiation. A major advantage of this strategy is a reduction of the conflict between resolution, time and field. This
reduction is achieved by task specific conditioning of sensors, indicator algorithms and actuators using suitable
uncertainty models. This contribution is focused on uncertainty models of sensors and actuators using the example of a
prototype multi-scale measurement system. The influence of the sensor parameters, object characteristics and
measurement conditions on the measurement reliability is investigated exemplary for the middle-scale sensor, a confocal
Holographic tweezers offer a very versatile tool in many trapping applications. Compared to tweezers working with acousto optical modulators or using the generalized phase contrast, holographic tweezers so far were relatively slow. The computation time for a hologram was much longer than the modulation frequency of the modulator. To overcome this drawback we present a method using modified algorithms which run on state of the art graphics boards and
not on the CPU. This gives the potential for a fast manipulation of many traps, for cell sorting for example, as well as for a real-time aberration control. The control of aberrations which can vary spatially or temporally is relevant to many real world applications. This can be accomplished by applying an iterative approach based on image processing.