Daniel Kayser

Scaled topometry in a multisensor approach

Reliable real-time surface inspection of extended surfaces with high resolution is needed in several industrial applications. With respect to an efficient application to extended technical components such as aircraft or automotive parts, the inspection system has to perform a robust measurement with a ratio between depth resolution and lateral extension of less than 10–6. This ratio is at least 1 order beyond the solutions that are offered by existing technologies. The concept of scaled topometry consists of a systematic combination of different optical measurement techniques with overlapping ranges of resolution systematically to receive characteristic surface information with the required accuracy. In such a surface inspection system, an active algorithm combines measurements on several scales of resolution and distinguishes between local fault-indicating structures with different extensions and global geometric properties. The first part of this active algorithm finds indications of critical surface areas in the data of every measurement and separates them into different categories. The second part analyzes the detected structures in the data with respect to their resolution, and decides whether a further local measurement with a higher resolution has to be performed. The third part positions the sensors and starts the refined measurements. The fourth part finally integrates the measured local dataset into the overall data mesh. We have constructed a laboratory setup capable of measuring surfaces with extensions up to 1500×1000×500 mm3 (in x, y, and z directions, respectively). Using this measurement system we are able to separate the fault-indicating structures on the surface from the global shape, and to classify the detected structures according to their extensions and characteristic shapes simultaneously. The level of fault-detection probability is applicable by input parameter control.

Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors

Abstract. The detection and classification of faults is a major taskfor optical nondestructive testing in industrial quality control. Inter-ferometric fringes, obtained by real-time optical measurement meth-ods, contain a large amount of image data with information aboutpossible defect features. This mass of data must be reduced forfurther evaluation. One possible way is the filtering of these imagesby applying the adaptive wavelet transform, which has been provedto be a capable tool in the detection of structures with definite spatialresolution. In this paper we show the extraction and classification ofdisturbances in interferometric fringe patterns, the application ofseveral wavelet functions with different parameters for the detectionof faults, and the combination of wavelet filters for fault classifica-tion. Examples of fringe patterns of known and varying fault param-eters are processed showing the trend of the extracted features inorder to draw conclusions concerning the relation between the fea-ture, the filter parameter, and the fault attributes. Real-time process-ing was achieved by importing video sequences in a hybrid opto-electronic system with digital image processing and an optical cor-relation module. The optical correlator system is based on liquid-crystal spatial light modulators, which are addressed with image andfilter data. Results of digital simulation and optical realization arecompared.

Fault detection in gray-value images of surfaces on different scales

The idea of scaled topometry is to organize systematically different optical measurement techniques with overlapping ranges of resolution in order to receive highly resolved surface information in a wide range of scales. In such a surface inspection system, measurements on different scales of resolution have to be combined by a discrimination algorithm which should be sensitive on faults independent on the scale of resolution. Starting from a global measurement with low resolution certain critical areas have to be detected in which a refined measurements has to be performed. This process of detection and refinement has to be repeated on different scales. The task of the discrimination algorithm should be the detection of critical structures and the determination of the necessary order of refinement in the resolution. For the reason of scale- independence a classical approach using the surface roughness is not suitable.

Application of wavelet filters for feature extraction in interferometric fringe patterns

The fast and reliable localization and classification of fault indicating fringe patterns in interferometric images is a major task in holographic non-destructive testing. For the purpose of feature extraction from gray value images, wavelet transformation has proved to be a suitable tool. In contrast to the Fourier transformation the local feature information will be preserved and furthermore the applied transforming wavelet can be adapted--under certain constraints--to the given problem.

Optical processing for the detection of faults in interferometric patterns

The detection and classification of faults is a major task for optical nondestructive testing in industrial quality control. Interferometric fringes, obtained by real-time optical measurement methods, contain a large amount of image data with information about possible defect features. This mass of data must be reduced for further evaluation. One possible way is the filtering of these images applying the adaptive wavelet transform. The wavelet transform has been proved to be a capable tool in the detection of structures with definite spatial resolution. In this paper it is shown the extraction and classification of disturbances in interferometric fringe patterns, the application of several wavelet functions with different parameters for the detection of faults, and the combination of wavelet filters for fault classification. Furthermore the implementation of complex valued wavelet filters and correlation filters is shown. We will present an algorithm to classify interferometric fringe patterns. In order to achieve real-time processing a hybrid opto-electronic system with a digital image processing and an optical correlation module is favored. The calculated wavelet filters are implemented into the optical correlator system that is based on liquid-crystal spatial light modulators. So, all discussed items were verified experimentally in the optical setup.

Scaled multisensor inspection of extended surfaces for industrial quality control

Reliable real-time surface inspection of extended surfaces with high resolution is needed in several industrial applications. With respect to an efficient application to extended technical components such as aircraft or automotive parts, the inspection system has to perform a robust measurement with a ratio of less then 10-6 between depth resolution and lateral extension. This ratio is at least one order beyond the solutions that are offered by existing technologies. The concept of scaled topometry consists of arranging different optical measurement techniques with overlapping ranges of resolution systematically in order to receive characteristic surface information with the required accuracy. In such a surface inspection system, an active algorithm combines measurements on several scales of resolution and distinguishes between local fault indicating structures with different extensions and global geometric properties. The first part of this active algorithm finds indications of critical surface areas in the data of every measurement and separates them into different categories. The second part analyses the detected structures in the data with respect to their resolution and decides whether a further local measurement with a higher resolution has to be performed. The third part positions the sensors and starts the refined measurements. The fourth part finally integrates the measured local data set into the overall data mesh. We have constructed a laboratory setup capable of measuring surfaces with extensions up to 1500mm x 1000mm x 500mm (in x-, y- and z-direction respectively). Using this measurement system we will be able to separate the fault indicating structures on the surface from the global shape and to classify the detected structures according to their extensions and characteristic shapes simultaneously. The level of fault detection probability will be applicable by input parameter control.

High-resolution measurement of extended technical surfaces with scalable topometry

The wide scale inspection of extended technical components with respect to the recognition of typical surface features (shape, texture, roughness) needs the combined application of different measurement techniques with new tools for the consistent analysis and description of the measuring results. The new concept of scaleable topometry meets the demands of wide scale surface topometry. Controlled by the evaluation of scale-independent surface features based on fractal geometry, different measurement techniques with subsequent lateral and depth resolution are applied to the surface. The result is a complete description of the surface covering a wide scale taking into account special regions of interest. The choice and orientation of the special measurement technique is supported by a new feature extraction method called the fractal pyramid. The advantages of the new concept are demonstrated on several technical components.

Dynamic classification of fringe patterns in holographic interferometry by optical wavelet filtering

In this paper the dynamic processing of interferometric fringe patterns obtained by real-time optical measurement methods like holographic interferometry is shown. A hologram of the tested component is superimposed with the hologram of the stressed component. The achieved fringe patterns vary according to the degree of stress applied. To evaluate these varying fringe patterns in real time, dynamic filtering is required. A hybrid opto-electronic system with a digital image processing and optical correlation module based on liquid-crystal spatial light modulators gives us the possibility to use dynamic filters and input images. In order to process interferometric fringes the adaptive wavelet transformation is applied. We will show two methods of dynamic filtering. Firstly a static filter is used to process varying fringe patterns. With this method changes of features in the fringe patterns can be observed correlating to changes of stress applied on the tested component. Another application of dynamic filtering uses a static input image and dynamic filters. This method is used for the classification of interferometric fringe patterns. A set of different wavelet filters is applied to the input image using the ability of the spatial light modulator to display images in video frame rates. Comparing the wavelet filters and the output images it is possible to assign the fringe patterns to a fault class.

Optische Verarbeitung von interferometrischen Streifenbildern und die Fehlererkennung durch Wavelet-Filterung (Optical Processing of Interferometric Fringes and Detection of Faults by Wavelet Filtering)

Abstract Das Erkennen und Klassifizieren von Fehlern ist eine wichtige Aufgabe der optischen zerstörungsfreien Materialprüfung in der industriellen Qualitätskontrolle. Eine Vielzahl von Daten über Fehler im Material sind in interferometrischen Streifenmustern enthalten. Diese Daten müssen für die weitere Auswertung reduziert werden. Eine Möglichkeit der Datenreduktion ist durch die Wavelet-Transformation gegeben. Fehler in interferometrischen Streifenmustern sollen durch Wavelet-Filter erkannt, lokalisiert und klassifiziert werden. Wavelet-Funktionen sind sowohl im Ortsraum als auch im Frequenzraum lokalisiert und eignen sich deshalb zur Extraktion von Merkmalen ohne Verlust der räumlichen Zuordnung. Eine Klassifizierung der Interferogramme ist möglich, da die Fehlerklassen charakteristische Eigenschaften haben, die mit bestimmten Klassen von Wavelet-Filtern erkannt werden können.

Detection of faults in interferometric fringe patterns by optical wavelet filtering

In this paper the dynamic processing of interferometric fringe patterns obtained by real-time optical measurement methods like holographic interferometry is shown. A hologram of the tested component is superimposed with the hologram of the stressed component. The achieved fringe patterns vary according to the degree of stress applied. To evaluate these varying fringe patterns in real time, dynamic filtering is required. A hybrid opto-electronic sytem with a digital image processing and optical correlation module based on liquid-crystal spatial light modulators gives us the possibility to use dynamic filters and input images. In order to process interferometric fringes the adaptive wavelet transformation is applied.